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On Defining Artificial Intelligence

(3):351–361. Turing, A. M. 1950. Computing machinery and intelligence. Mind LIX:433–460. Tversky, A., and Kahneman, D. 1974. Judgment under uncertainty: heuristics and biases. Science 185:1124–1131. von Neumann, J. 1958. The Computer and the Brain . New Haven, CT: Yale University Press. Wang, P., and Goertzel, B. 2007. Introduction: Aspects of artificial general intelligence. In Goertzel, B., and Wang, P., eds., Advance of Artificial General Intelligence . Amsterdam: IOS Press. 1–16. Wang, P., and Hammer, P. 2015. Issues in Temporal and Causal

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Causal Mathematical Logic as a guiding framework for the prediction of “Intelligence Signals” in brain simulations

Hartonen, T.; and Annila, A. 2012. Natural networks as thermodynamic systems. Complexity. 18(2)53-62. Haufe, S.; Tomioka, R.; and et al. 2010. Localization of class-related mu-rhythm desynchronization in motor imagery based brain-computer interface sessions. Conf Proc IEEE Eng Med Biol. Soc 5137-40. Hawkins, J. 2006. Hierarchical Temporal Memory (HTM) A new computational paradigm based on cortical theory. IBM Hawkins, J.; and Blakeslee, S. 2004. On Intelligence: How a New Understanding of the Brain will Lead to the Creation of Truly

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E-ETL: Framework for Managing Evolving ETL Workflows

-ETL: Framework For Managing Evolving ETL Processes.," in Proc. of Ph.D. Students in Information and Knowledge Management Work- shop (PIKM), pp. 59{66, ACM Press, 2011. [9] J. Eder, C. Koncilia, and T. Morzy, \The COMET Metamodel for Temporal Data Warehouses," in Proc. of Int. Conf. on Advanced Information Systems Engineering (CAiSE), pp. 83{99, Springer-Verlag, 2002. [10] E. A. Rundensteiner, A. Koeller, and X. Zhang, \Maintaining data warehouses over changing information sources," Communications of the ACM, vol. 43, no. 6, pp. 57{62, 2000.

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An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

-1780. [50] Yang, X.-S. and S. Deb. Cuckoo search via Lévy flights. in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on . IEEE,2009, 210-214. [51] Žalik, K.R., An efficient k′-means clustering algorithm. Pattern Recognition Letters , 29 , 9, 2008, 1385-1391. [52] Zhang, B., M. Hsu, and U. Dayal, K-Harmonic Means -A Spatial Clustering Algorithm with Boosting , in Temporal, Spatial, and Spatio-Temporal Data Mining , J. Roddick and K. Hornsby, Editor, Springer Berlin Heidelberg. 2001, 31-45.

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The Applicability Of SWE In Polish Spatial Data Infrastructures - The Example Of The SensorML Language

and hydrogeology, Annals of Geomatics , IX , 4, 2011, 105-115. [27] Prandi F., De Amicis R., Conti G., Debiasi A., Use of OGC Web Standard for a Spatio-Temporal Enabled SDI for Civil Protection , Proceedings of the 17th International Conference on 3D Web Technology, New York, 2012. [28] Rossa M., Gogołek W., Łukasiewicz A., Geostandardy, metadane i dyrektywa INSPIRE. Poradnik metodyczny Zintegrowanego Systemu Kartografii Geologicznej IKAR. Państwowy Instytut Geologiczny, Warszawa, 2009. [29] Rossa M., Aplikacje GML dla środowiska na

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Tools for Distributed Systems Monitoring

., Distributed, application-level monitoring for heterogeneous clouds using stream processing, Future Generation Computer Systems, vol. 29, pp. 2103-2114, 2013. [11] Spellmann A., Gimarc R., Capacity Planning: A Revolutionary Approach for Tomorrow’s Digital Infrastructure, Computer Measurement Group Conference, La Jolla, California, USA, 2013. [12] Terenziani P., Coping with Events in Temporal Relational Databases, IEEE Trans. Knowledge and Data Eng., vol. 25, no. 5, pp. 1181-1185, 2013. [13] Tierney B., Crowley B., Gunter D

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Towards a Process Calculus for Rest: Current State of the Art

: Proceedings of the International Conference on Semantic Computing. pp. 469-476. ICSC ’07, IEEE Computer Society, Washing- ton, DC, USA (2007) [35] Pautasso, C., Zimmermann, O., Leymann, F.: Restful web services vs. big’web services: making the right architectural decision. In: Proceedings of the 17th international conference on World Wide Web. pp. 805-814. ACM (2008) [36] Plotkin, G.: A structural approach to operational semantics (1981) [37] Pnueli, A.: The temporal logic of programs. In: Foundations of Computer Sci- ence

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SoC as IoT sensor network hub

, R. Culmone, L. Mostarda, “Modeling temporal aspects of sensor data for MongoDB NoSQL database”, J. Big Data, 2017, pp. 4:8 [5] L. Atzori, A. Iera, G. Morabito, “The Internet of Things: A Survey”, Computer Networks, vol. 54, no. 15, pp. 2787-2805, Oct., 2010. N.Q. Mehmood, R. Culmone, L. Mostarda, “Modeling temporal aspects of sensor data for MongoDB NoSQL database”, J. Big Data, 2017, pp. 4:8 [6] M. Roopaei, P. Rad and K. R. Choo, “Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging,” in IEEE Cloud Computing, vol

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A Multilayered Self-Learning Spiking Neural Network and its Learning Algorithm Based on ‘Winner-Takes-More’ Rule in Hierarchical Clustering

References Bohte S.M., Kok J.N., La Poutre H. Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks // IEEE Transactions on Neural Networks. - 13 (March, 2002), P. 426-435. Bodyanskiy Ye., Dolotov A., Viktorov Ye. Heterogeneous spiking neural network in clustering problem // Bulletin of Lviv Polytechnic National University. Computer Science and Information Technologies. - 604 (2008), P. 84-90. Image clustering with spiking neuron network

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Spatiotemporal Aspects of Big Data

, Moving Objects Databases , 1st ed. Morgan Kaufmann, 2005. [9] S. Rathee, and A. Yadav, “Survey on Spatio-Temporal Database and Data Models with relevant Features,” International Journal of Scientific and Research Publications , vol. 3, no. 1, pp. 152–156, 2013. [10] I. Ali, H. Samoon, and A. Khan, “23 killed as monsoon rains lash Karachi,” Dawn News, 2017. [Online]. Available: https://www.dawn.com/news/1355132 . [Accessed: 01-Nov-2017]. [11] “Temporal Database,” Teradata Database, Tools and Utilities Release 16.00. [Online]. Available: https

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