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Alexandru Lodin, Lacrimioara Grama and Corneliu Rusu

REFERENCES [1] J. Vlach and K. Singhal, Computer Methods for Circuit Analysis and Design, 2nd ed. New York, NY, USA: John Wiley & Sons, Inc., 1993. [2] L. D. Chua and P.-M. Lin, Computer-Aided Analysis of Electronic Circuits. Englewood Cliffs, NJ: Prentice Hall, 1975. [3] C. Rusu, L. Grama, and J. Takala, “SPICE simulation of analog filters: A method for designing digital filters,” in Proceedings EUROCAST 2009, Gran Canaria, Feb. 2009, pp. 179–180. [4] L. Grama, A. Lodin, C. Rusu, and J. Takala, “Method to convert analog filters to digital

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Obunike Chinazor Lady-Franca

entrepreneurship in Nigeria; Ibadan : Ibadan University Press. Akpor-Robaro, M. O. M. (2012). The impact of socio-cultural environment on entrepreneurial emergence, a theoretical analysis of Nigerian society. European Journal of Business and Management 4(16): 172–182. Aldrich, H.; Zimmer, C. (1986b). Entrepreneurship through social networks. Cambridge, Massachusetts: Ballinger Publishing Company. Analoui, F.; Moghimi S. M.; Khanifar, H. (2009), Public sector managers and entrepreneurship in Islamic republic of Iran. Journal of Management Development 28

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Guido Bologna and Yoichi Hayashi

, 2008 [11] L. K. Hansen, P. Salamon, Neural network ensembles, IEEE transactions on pattern analysis and machine intelligence 12, 1990, 993-1001 [12] Z.-H. Zhou, Y. Jiang, S.-F. Chen, Extracting symbolic rules from trained neural network ensembles, Artificial Intelligence Communications 16 , 1, 2003 3-16. [13] R. Setiono, B. Baesens, C. Mues, Recursive neural network rule extraction for data with mixed attributes, Neural Networks, IEEE Transactions on 19 , 2, 2008, 299-307 [14] A. Hara, Y. Hayashi

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Balázs Telegdy

References Bonacich P. and P. Lloyd (2004) ‘Calculating status with negative relations’. Social Networks, 26(4): 331 - 338. Bryson, B. (1996) ‘Anything But Heavy Metal: Symbolic Exclusion and Musical Dislikes’. American Sociological Review, 61(5): 884 - 899. Burt, R.S. (1990) ‘Detecting Role Equivalence’. Social Networks, 12(1): 83 - 94. Cartwright, D. and F. Harary (1956) ‘Structural balance: a generalization of Heider's theory’. Psychological Review, 63(5): 277 - 293. Csaba, Z.L. and J. Pál (2010) ‘How Negative Networks Are

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Marcin Kamiński

–261. Sakata, S., Ashida, F., Kojima, T. and Zako, M. (2008). Three-dimensional stochastic analysis using a perturbation-based homogenization method for elastic properties of composite material considering microscopic uncertainty, International Journal of Solids and Structures 45 (3–4): 894–907. Schueller, G. (2007). On the treatment of uncertainties in structural mechanics and analysis, Computers and Structures 85 (5–6): 235–243. Shachter, R.D., D’Ambrosio, B. and Del Favero, B. (1990). Symbolic probabilistic inference in belief networks, Proceedings of

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Yoichi Hayashi, Yuki Tanaka, Tomohiro Takagi, Takamichi Saito, Hideaki Iiduka, Hiroaki Kikuchi, Guido Bologna and Sushmita Mitra

. 299–307, 2008. [14] Setiono, R., and Liu, H. Symbolic representation of neural networks. IEEE Computer 29, No. 3, pp. 71–77, 1996. [15] Gupta, A, Park, S. and Lam, S.M. Generalized analytic rule extraction for feedforward neural networks, IEEE Trans. Knowledge and Data Engineering, 11, pp.985-991, 1999. [16] Etchell, T.A. and Lisboa, J.P.G., Orthogonal search-based rule extraction (OSRE) for trained neural-networks: A practical and efficient approach, IEEE Trans. Neural Networks 17, pp.374-384, 2006. [17] Hansen, L.K., and Salamon, P., Neural

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Tomáš Fabšič, Ondrej Gallo and Viliam Hromada

REFERENCES [1] BALDI, M.—CHIARALUCE, F.: Cryptanalysis of a new instance of McEliece cryptosystem based on QC-LDPC codes, in: Proceedings IEEE ISIT ’07, Nice, France, 2007, pp. 2591–2595. [2] BALDI, M.—BODRATO, M.—CHIARALUCE, F.: A new analysis of the McEliece cryptosystem based on QCLDPC codes, in: 6th Internat. Conf. on Security and Cryptography for Networks—SCN ’08 (R. Ostrovsky et al., eds.), Lecture Notes in Math., Vol. 5229, Springer-Verlag, Berlin, 2008, pp. 246–262. [3] BitPunch, [4] HEYSE, S

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Igor Aizenberg, Antonio Luchetta, Stefano Manetti and Maria Cristina Piccirilli

-41. [11] I. Aizenberg, and C. Moraga, Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm, Soft Computing, 11, no. 2, 169-183, Jan. 2007. [12] N. Sen and R. Saeks, Fault Diagnosis for Linear System via Multifrequency Measurement”, IEEE trans. Circuits and Systems, vol. CAS-26, pp.457 - 465, 1979. [13] N. N. Aizenberg, L. Ivaskiv, D. A. Pospelov, and G.F. Hudiakov, Multivalued Threshold Functions. II. Synthesis of Multivalued Threshold Elements, Cybernetics and Systems Analysis, vol. 9, no. 1

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Darko Šošić and Ivan Škokljev

Markets, International Journal of Electrical Power & Energy Systems 44 No. 1 (2013), 308-317. [10] ˇ SKOKLJEV, I.-TOˇ SI´C, D. : A New Symbolic Analysis Approach to the DC Load Flow Method, Electric Power Systems Research 40 No. 2 (1997), 127-135. [11] ˇ SKOKLJEV, I. : Successive Expansion Method of Network Planning Applying Symbolic Analysis Method, European Transactions on Electrical Power 14 No. 4 (2002), 259-267. [12] DEZAKI, H. H.-ABYANEH, H. A.-AGHELI, A.-MAZLUMI, K. : Optimized Switch Allocation to Improve the

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Abdelaziz Zaidi, Belkacem Ould Bouamama and Moncef Tagina

, John Wiley, Hoboken, NJ. Arroyo-Figueroa, G. and Sucar, L. E. (1999). A temporal Bayesian network for diagnosis and prediction, Uncertainty in Artificial Intelligence. Proceedings of the 15th UAI Conference, Stockholm, Sweden , pp. 13-20. Ben Salem, A., Muller, A. and Weber, P. (2006). Dynamic Bayesian networks in system reliability analysis, 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Beijing, China , Vol. 6, pp. 481-486. Bobbio, A., Portinale, L., Minichino, M. and