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Learning Fast Quadruped Robot Gaits with the RL PoWER Spline Parameterization

, J. C. Zagal, H. Lipson. Evolving Robot Gaits in Hardware: the Hyperneat Generative Encoding Vs. Parameter Optimization. - In: Proceedings of the 20th European Conference on Artificial Life, 2011. 13. Zykov, V., J. Bongard, H. Lipson. Evolving Dynamic Gaits on a Physical Robot. - In: Proceedings of Genetic and Evolutionary Computation Conference, Late Breaking Paper, GECCO, Vol. 4 , 2004.

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A Novel Fuzzy Clustering Recommendation Algorithm Based On Pso

Graphs for Testing Community Detection Algorithms. - Physical Review E, Vol. 78, 2008, No 4. 18. Zachary, W. W. An Information Flow Model for Confict and Fission in Small Groups. - J. Anthropol. Res., 1977, 452-473. 19. Lusseau, D., K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, S. M. Dawson. The Bottlenose Dolphin Community of Doubtful Sound Features a Large Proportion of Long-Lasting Associations. - Behavioral Ecology and Sociobiology, Vol. 54, 2003, No 4, 396-405. 20. Newman, M. E. J. Finding Community Structure

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Modules for Rapid Application Development of Web-Based Information Systems (RADWIS)

. – Cybernetics and Information Technologies, Vol. 11 , 2011, No 2, pp. 46-64. 4. Stancheva, N., A. Stoyanova-Doycheva, S. Stoyanov, I. Popchev, V. Ivanova. A Model for Generation of Test Questions. – Compt. Rend. Acad. bulg. Sci., Vol. 70 , 2017, No 5, pp. 619-630. 5. Stancheva, N., A. Stoyanova-Doycheva, S. Stoyanov, I. Popchev, V. Ivanova. An Environment for Automatic Test Generation. – Cyberbetics and Information Technologies, Vol. 17 , 2017, No 2, pp. 183-196. 6. Stoyanov, S., I. Ganchev, I. Popchev, M. O’Droma. An Approach for the Development of

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Low Cost Locating Method of Wireless Sensor Network in Precision Agriculture

-151. 4. Guibas, L. J. Tracking, Sensing and Reasoning with Relations. – IEEE Signal Processing Manazine, Vol. 19 , February 2002, pp. 73-85. 5. Rabaey, M. J., M. Ammer. Picoradio Supports Ad Hoc Ultra-Low Power Wireless Networking. –IEEE Computer Magazine, Vol. 33 , May 2000, pp. 42-48. 6. Warneke, B., M. Last. Communicating with a Cubic-Millimeter Computer. – IEEE Computer, Vol. 34 , May 2001, pp. 44-51. 7. Shih, E., C. Ickesn. Physical Layer Driven Protocol and Algorithm Design for Energy-Efficient Wireless Sensor Networks. – In: Proc. of ACM

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

References 1. Yu, Kanhua. Urban Planning Support System Based on Three Dimensional GIS. - IAES TELKOMNIKA Indonesian Journal of Electrical Engineering, Vol. 12, 2014, No 5, pp. 3928-3935. 2. Li, Chang, W. Shi, F. Li. The Scheme and the Preliminary Test of Object-Oriented Simultaneous 3D Geometric and Physical Change Detection Using GIS-Guided Knowledge. - Telkomnika - Indonesian Journal of Electrical Engineering, Vol. 11, 2013, No 12, pp. 7462-7469. 3. Toscano, E. S. A., V. Sorengo. 2Dand 3D GIS-Based Geological

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Optimizing Signal De-Noising Algorithm for Acoustic Emission Leakage of Wavelet

/Shell Leakage Flow on Heat Transfer in Shell-and-Tube Heat Exchangers. – Experimental Thermal and Fluid Science, Vol. 8 , January 1994, Issue 1, pp. 10-20. 9. M. F. El-Amin, H. Kanayama. Boundary Layer Theory Approach to the Concentration Layer Adjacent to a Ceiling Wall at Impinging Region of a Hydrogen Leakage. – International Journal of Hydrogen Energy, Vol. 33 , November 2008, Issue 21, pp. 6393-6400. 10. Or, S. W., H. L. W. Chan, C. L. Choy. P(VDF-TrFE) Copolymer Acoustic Emission Sensors. – Sensors and Actuators A: Physical, Vol. 80 , 15 March 2000, Issue

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Mitigation of Distributed Denial of Service Attacks in the Cloud

3. Alosaimi, W., K. Al-Begain. A New Method to Mitigate the Impacts of Economical Denial of Sustainability Attacks Against the Cloud. - In: Proc. of 14th Annu. Post. Grad. Symp. Converg. Telecommun. Netw. Broadcating. Liverpool John Moores University, Liverpool, UK, 2013, pp. 116-121. 4. Aslan, T. Cloud Physical Security Considerations. IBM Cloud, 2012 [Cited 15 February 2017]. 5. Beitollahi, H., G. Deconinc k. FOSe L: Filtering by Helping an

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Neural Network Modelling for Sports Performance Classification as a Complex Socio-Technical System

Entertainment Artist Using Clustering and Classification Approaches,” International Journal of Electronic Business Management, vol. 10, no. 4, 2012, pp. 274-285. [24] B. Ofoghi, J. Zeleznikowa, C. MacMahon and M. Raab, “Data Mining in Elite Sports: A review and a Framework,” Measurement in Physical Education and Exercise Science, vol. 17, 2013, pp. 171-186. Available: EBSCO. [25] S. Provorovs and A. Borisov, “Use of Linear Genetic Programming and Artificial Neural Network Methods to Solve

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Security of Low Computing Power Devices: A Survey of Requirements, Challenges & Possible Solutions

Intelligence and Informatics. Advances in Intelligent Systems and Computing, Springer, Singapore. Vol. 507 . 2016, pp. 221-230. 68. Wang, Q., T. Dunlap, Y. Cho, G. Qu. DoS Attacks and Countermeasures on Network Devices. – In: Proc. of 26th Wireless and Optical Communication Conference (WOCC’17), Newark, 2017, pp. 1-6. 69. Gassend, B., D. Clarke, M. V. Dijk, S. Devadas. Silicon Physical Random Functions. – In: Proc. of 9th ACM Conference on Computer and Communications Security. ACM, 2002, pp. 148-160. 70. Standaert, F. X. Introduction to Side

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Methods and Practices for Institutional Benchmarking based on Research Impact and Competitiveness: A Case Study of ShanghaiTech University

a small-scale, internationalized, and first-class research institution aiming at tackling globally advanced and hard-fought scientific challenges. It focuses its research only on a few selected subject fields in Physical Sciences & Technologies, Biological Sciences and Technologies, and Information Sciences and Technology, and avoids trying to cover complete disciplines. As of June 2019, it has fewer than 200 faculty members, no more than 500 undergraduate and 1000 graduate enrollments yearly. The university evaluates the research performance of itself, its

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