G. Denaro, A. Polini, and W. Emmerich, "Early performance testing of distributed software applications," in 4th International Workshop on Software and Performance (WOSP '04) , January 2004. USA: ACM, 2004, pp. 94-103.
D. P. Olshefski, J. Nieh, and D. Agrawal, "Inferring client response time at the web server," in International Conference on Measurements and Modeling of Computer Systems (SIGMETRICS 2002) , June 2002. USA: ACM, 2002, pp. 160-171.
B. M. Subraya
Kevin Gluck, Clayton Stanley, L. Moore, David Reitter and Marc Halbrügge
Moore, L. R. 2010. Cognitive Model Exploration and Optimization: A New Challenge for Computational Science. In T. Jastrzembski (Ed.), Proceedings of the 19th Behavior Representation in Modeling and Simulation (BRIMS) Conference . Charleston, SC.
Moore, L. R., Kopala, M., Mielke, T., Krusmark, M., and Gluck, K. A. (2010). Simultaneous Performance Exploration and Optimized Search with Volunteer Computing. In Proceedings of the ACM International Symposium on High Performance Distributed Computing (HPDC) . Chicago, IL
] GitHub, “Chart.js – Simple HTML5 Charts using the <canvas> tag”. [Online]. Available: https://github.com/chartjs/Chart.js
 npm, “redis”. [Online]. Available: https://www.npmjs.com/package/redis
 G. A. Francia and R. R. Francia, “An Empirical Study on the Performance of Java/.Net Cryptographic APIs,” Information Systems Security , vol. 16, no. 6, pp. 344–354, Dec. 2007. https://doi.org/10.1080/10658980701784602
 O. Hamed, “Performance Prediction of Web Based Application Architectures Case Study: .NET vs. Java EE.,” International
Convolutional neural networks (CNN) is a contemporary technique for computer vision applications, where pooling implies as an integral part of the deep CNN. Besides, pooling provides the ability to learn invariant features and also acts as a regularizer to further reduce the problem of overfitting. Additionally, the pooling techniques significantly reduce the computational cost and training time of networks which are equally important to consider. Here, the performances of pooling strategies on different datasets are analyzed and discussed qualitatively. This study presents a detailed review of the conventional and the latest strategies which would help in appraising the readers with the upsides and downsides of each strategy. Also, we have identified four fundamental factors namely network architecture, activation function, overlapping and regularization approaches which immensely affect the performance of pooling operations. It is believed that this work would help in extending the scope of understanding the significance of CNN along with pooling regimes for solving computer vision problems.
-Learning Digital Spiking Neuromorphic Processor in 28 nm CMOS , IEEE Transactions on Biomedical Circuits and Systems, 2018.
 Luo Q. Fu, Y., Liu J., Qiu J. Bi, S., Cao Y., Ding X., Improving Learning Algorithm Performance for Spiking Neural Networks , 17th IEEE International Conference on Communication Technology, 2017.
 Hodgkin A. L., Huxley A. F., A quantitative description of membrane current and its application to conduction and excitation in nerve , The Journal of Physiology, vol. 117, pp. 500-544, 1952.
 Chang R. Hu, S., Wang H., Huang J. He, Q
 Baek S., Choi B.D., Performance of an effcient sleep mode operation for IEEE 802.16m, Journal of Industrial and Management Optimization , 7, 3, 2011, 623-639.
 Chen C.Y., Hsu C.H., Feng K.T., Performance analysis and comparison of sleep mode operation for IEEE 802.16m advanced broadband wireless networks, Proceedings of the IEEE 21st International Symposium on Personal Indoor and Mobile Radio Communications , 2010, 1425-1430.
 Han K., Choi S., Performance Analysis of Sleep Mode Operation in IEEE 802.16e Mobile BroadBand Wireless
., Yaksha: A self-tuning controller for managing the performance of 3-tiered web sites, Proceedings of International Workshop on Quality of Service , 2004, 47-58.
 Kundu A., Banerjee A.D., Saha P., Introducing New Services in Cloud Computing Environment, International Journal of Digital Content Technology and its Applications , 4, 5, 2010, 143-152.
 Reddy K.V., Rao B., Reddy L.S.S.,Kiran P.S., Research Issues in Cloud Computing, Global Journal of Computer Science and Technology , 11, 11, 2011, 59-64.
 Urgaonkar B
Information security governance as key performance indicator for financial institutions
Due to their nature financial institutions and their performance are in constant focus of attention from different stakeholder groups. These groups according to their functions and interests are implementing different sets of key performance indicators for financial institution performance assessment. In the proposed paper authors present a hypothesis of information security governance being a financial institution key performance indicator. Authors provide high level overview of existing situation in key performance indicator domain for financial institutions. The overview of stakeholder groups interested in financial institution performance management is provided. In the same way as corporate governance is treated as financial and operational performance reflecting and influencing factor, information security governance as a component of corporate governance, according to authors' opinion, should be treated as key performance indicator for financial institutions. In the paper the most indicative financial performance indicators as well as their calculation methods are defined for financial institutions. The paper contains overview of information security assessment models and researches in this field. Authors have chosen information security maturity model to use in testing hypothesis. The paper contains description of calculation methodology for financial performance indicators and information security maturity indicators. The hypothesis has been proved performing analysis of correlation between calculated financial performance indicators and information security governance model indicators for chosen Latvian financial institutions.
Christian Lebiere, Cleotilde Gonzalez and Walter Warwick
). Slope of inflow impacts dynamic decision making. In Proceedings of the 25th International Conference of the System Dynamics Society (pp. 79). Boston, MA: System Dynamics Society.
Foyle, D. & Hooey, B. (2008). Human Performance Modeling in Aviation. Mahwah, NJ: Erlbaum.
Gluck, K, & Pew, R. (2005). Modeling Human Behavior with Integrated Cognitive Architectures. Mahwah, NJ: Erlbaum.
Gonzalez, C., & Dutt, V. (2007). Learning to control a dynamic task: A system dynamics cognitive model of
beer game. In Lovett, M.; Schunn, C.; Lebiere, C.; and Munro, P., eds., Proceedings of the Sixth International Conference on Cognitive Modeling , 178-183.
Meyer, D. E., and Kieras, D. E. 1997. A computational theory of executive cognitive processes and multiple-task performance 1. Basic mechanisms. Psychological Review 104:3-65.
Newell, A. 1990. Unified Theories of Cognition. Cambridge, MA: Harvard University Press.
Peebles, D., and Cheng, P. C.-H. 2003. Modeling the effect of task and