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[1] Khcherif, R., Gammoudi, M. N. and Jaoua, A. Using Difunctional Relations in Information Organization, Journal of Information Sciences, 125, pp. 153 166, 2000. [2] Jaoua, A. and Elloumi, S. Galois Connection, Formal Concepts and Galois Lattice in Real Relations: Application in a Real Classifier, The Journal of Systems and Software, 60, pp. 149 163, 2002. [3] Mineau, G. W. and Godin, R. Automatic Structuring of Knowledge Bases by Conceptual Clustering, IEEE Transactions On Knowledge and Data Engineering, 7(5), pp. 824 829, 1995. [4] Ben Yahia, S., Arour, K., Slimani, A. and Jaoua, A. Discovery of Compact Rules in Relational Databases, Information Journal, 3(4), pp. 497 511, 2000. [5] Ben Yahia, S. and Jaoua, A. Discovering Knowledge from Fuzzy Concept Lattice, in Kandel, A. Last, M. and Bunke, H. (Eds), Data Mining and Computational Intelligence, Studies in Fuzziness and Soft Computing, 68, pp. 167 190, Physica Verlag, Heidelberg, 2001. [6] Al-Rashdi, A., Al-Muraikhi, H., Al-Subaiey, M., Al-Ghanim, N. and Al-Misaifri, S. Knowledge Extraction and Reduction System (K.E.R.S.), Senior project, Computer Science Department, University of Qatar, June 2001. [7] Maddouri, M., Elloumi, S. and Jaoua, A. An Incremental Learning System for Imprecise and Uncertain Knowledge Discovery, Information Science Journal, 109, pp. 149 164, 1998. [8] Alsuwaiyel, M. H. Algorithms, Design Techniques and Analysis, Word Scientific, 1999. [9] Davey, B. A. and Priestley, H. A. Introduction to Lattices and Order, Cambridge Mathematical Textbooks, 1990. [10] Ganter, B. and Wille, R. Formal Concept Analysis, Springer Verlag, 1999. [11] Schmidt, G. and Str hlein, S. Relations and Graphs, Springer Verlag, 1989. [12] Jaoua, A., Bsaies, K. and Consmtini, W. May Reasoning be Reduced to an Information Retrieval Problem, International Seminar on Relational Methods in Computer Science, Quebec, Canada, 1999. [13] Jaoua, A., Boudriga, N., Durieux, J. L. and Mili, A. Regularity of Relations: A Measure of Uniformity, Theoretical Computer Science, 79, pp. 323 339, 1991. [14] Riguet, J. Relations binaires, fermetures et correspondences de Galois, Bulletin de la societ Mathematique de France, pp. 114 155, 1948. [15] Belkhiter, N., Bourhfir, C., Gammoudi, M. M., Jaoua, A., Le Thanh, N. and Reguig, M. Decomposition Rectangulaire Optimale d une Relation Binaire: Application aux Bases de Donnees Documentaires, Canadian Journal:INFOR, 32(1), pp. 33 54, 1994. [16] Jaoua, A., Belkhiter, N., Desharnais, J. and Moukam, T. Properties of Difunctional Dependencies in Relational Database, Canadian Journal INFOR, 30(1), pp. 297 315, 1992. [17] Maddouri, M., Elloumi, S. and Jaoua, A. An Incremental Learning for Imprecise and Uncertain Knowledge Discovery, Journal of Information Sciences, 109, pp. 149 164, 1998. [18] ElMasri, R. and Navathe, S. B. Fundamentals of Database Systems, third edition, Addison-Wesley, 2000. [19] Mcleod, R., Management Information Systems: A Study of Computer Based Information Systems, seventh edition, Simon and Schuster, 2000.
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[20] Jaoua, A., Ounalli, H. and Belkhiter, N. Automatic Entity Extraction From an N -ary Relation: Towards a General Law for Information Decomposition, International Journal of Information Science, 87(1 3), pp. 153 169, 1995. [21] B la d Ajroud, H., Jaoua, A. and Kaabi, S. Classes extraction from procedural programs, Information Sciences, 140(3 4) pp. 283 294, 2002. [22] Belaid, H. and Jaoua, A. Abstraction of objects by conceptual clustering, Journal of Information Sciences, 109, pp. 79 94, 1998. [23] A Text Mining System DIREC: Discovering Relationships between Keywords by Filtering, Extracting and Clustering, [24] Semi-Automatic Indexing of Multilingual Documents and Optimal Rectangle, cs/pdf/9902/9902022.pdf. [25] Natural Language Techniques and Text Mining Applications, [26] Tutorial: Text Analyst, [27] Mosaid, T., Hassan, F., Saleh, H. and Abdullah, F. Conceptual Text Mining: Application for Text Summarization, Senior Project, University of Qatar, January 2004.
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The world of digital communications has received much attention in the last few years. Extraordinary advances in both laser and semiconductor technologies have favored the development of new communication systems. However, it was the emergence of the Internet and the Web that created the largest impact on the telecommunication networks. Their open architecture has provided companies and users with promising opportunities for both e-commerce and e-services (e-banking, e-trading, e-training and others). However, these systems have also proved to be quite vulnerable and, consequently, new challenges must be faced. A crucial problem in all communication technologies is security. In this work, two different chaotic schemes for cryptographic communications with semiconductor lasers are described. Both approaches consist of an optical fiber communication network in which the transmitter and the receiver are both semiconductor lasers subjected to phase-conjugate feedback. The laser parameters are carefully chosen in such a way that the lasers exhibit a chaotic behavior, which is used to mask the message from the transmitter to the receiver. Thus, the laser parameters serve as the encryption key. In the first scheme, chaotic masking, the message is added to the chaotic output of the transmitter and then sent to the receiver, which synchronizes only with the chaotic component of the received signal. The message is recovered by a simple subtraction of the synchronized signal from the transmitted one. In the second scheme, chaotic switching, the information is binary and switches the transmitted signal between two different attractors associated with different chaotic receivers. Potential applications of these schemes, as well as their extraordinary advantages in comparison to other cryptographic schemes, are also discussed.
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