Grace Kamulegeya, Raymond Mugwanya and Regina Hebig
 Abran A. Software metrics and software metrology . John Wiley & Sons, 2010.
 Albrecht A. J. and Gaffney J. E. Software function, source lines of code, and development e ort prediction: a software science validation. IEEE transactions on software engineering , (6):639–648, 1983.
 Bajwa S. S., Gencel C., and Abrahamsson P. Software product size measurement methods. In Proceedings of International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement, IWSM
Alexey Grigorev, Alexey Lysenko, Igor Kochegarov, Vladimir Roganov and Jurijs Lavendels
 R. C. Gonzalez and R. E. Woods, Digital image processing. New Jersey, USA: Prentice Hall, 2008.
 G. V. Tankov, S. A. Brostislov, N. K. Yurkov and A. V. Lysenko, “Information-measuring and operating systems to test for the effects of vibration,” in 2016 International Siberian Conference on Control and Communications , SIBCON, May 2016. https://doi.org/10.1109/SIBCON.2016.7491679
 P. H. Rogers and H. M. Cox, “Noninvasive vibration measurement system and method for measuring amplitude of vibration of tissue in an object
 Badrinarayanan V., Kendall A., and Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. arXiv:1511.00561 [cs] , Nov. 2015. arXiv: 1511.00561.
 Chen L.-C., Papandreou G., Kokkinos I., Murphy K., and Yuille A. L. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. arXiv:1606.00915 [cs] , June 2016. arXiv: 1606.00915.
 Fisher R. A. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics , 7(2):179–188, Sept
for Instrumentation and Measurement Workshop 2017.
 Sharma S., Mehra R., Breast cancer histology images classification: Training from scratch or transfer learning?, ICT Express, 4 , 4, 2018, 247-254.
 Shi Z., Ye Y., Wu Y., Rank-based pooling for deep convolutional neural networks, Neural Networks, 83 , 2016, 21-31.
 Springenberg J. T., Dosovitskiy A., Brox T., Riedmiller M., Striving for simplicity: The all convolutional net, arXiv preprint arXiv:1412.6806, 2014,
 Srivastava N., Hinton G., Krizhevsky A., Sutskever I
Miroslaw Staron, Wilhelm Meding, Ola Söder and Magnus Bäck
industrial case study. In Proceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement , IWSM Mensura ‘17, pages 23–32, New York, NY, USA, 2017. ACM.
 Nicolette D. Software development metrics . Manning, 2015.
 Organization I. S. and Commission I. E. Software and systems engineering, software measurement process. Technical report, ISO/IEC, 2007.
 Perry D. E., Porter A., Wade M. W., Votta L. G., and Perpich J. Reducing inspection interval in large
Context. Software data collection precedes analysis which, in turn, requires data science related skills. Software defect prediction is hardly used in industrial projects as a quality assurance and cost reduction mean. Objectives. There are many studies and several tools which help in various data analysis tasks but there is still neither an open source tool nor standardized approach. Results. We developed Defect Prediction for software systems (DePress), which is an extensible software measurement, and data integration framework which can be used for prediction purposes (e.g. defect prediction, effort prediction) and software changes analysis (e.g. release notes, bug statistics, commits quality). DePress is based on the KNIME project and allows building workflows in a graphic, end-user friendly manner. Conclusions. We present main concepts, as well as the development state of the DePress framework. The results show that DePress can be used in Open Source, as well as in industrial project analysis.
Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) task
This paper describes the creation of a cognitive model submitted to the ‘Dynamic Stocks and Flows’ (DSF) modeling challenge. This challenge aims at comparing computational cognitive models for human behavior during an open ended control task. Participants in the modeling competition were provided with a simulation environment and training data for benchmarking their models while the actual specification of the competition task was withheld. To meet this challenge, the cognitive model described here was designed and optimized for generalizability. Only two simple assumptions about human problem solving were used to explain the empirical findings of the training data. In-depth analysis of the data set prior to the development of the model led to the dismissal of correlations or other parametric statistics as goodness-of-fit indicators. A new statistical measurement based on rank orders and sequence matching techniques is being proposed instead. This measurement, when being applied to the human sample, also identifies clusters of subjects that use different strategies for the task. The acceptability of the fits achieved by the model is verified using permutation tests.
Existing information technology tools are harnessed and integrated to provide digital specification of human consciousness of individual persons. An incremental compilation technology is proposed as a transformation of LifeLog derived persona specifications into a Canonical representation of the neocortex architecture of the human brain. The primary purpose is to gain an understanding of the semantical allocation of the neocortex capacity. Novel neocortex content allocation simulators with browsers are proposed to experiment with various approaches of relieving the brain from overload conditions. An IT model of the neocortex is maintained, which is then updated each time new stimuli are received from the LifeLog data stream; new information is gained from brain signal measurements; and new functional dependencies are discovered between live persona consumed/produced signals
Briggman, K.; Helmstaedter, M.; and Denk, W. 2011. Wiring specificity in the direction-selectivity circuit of the retina. Nature 471:183-188.
Deca, D. 2012. Available Tools for Whole Brain Emulation. International Journal of Machine Consciousness 4:67. doi: 10.1142/S1793843012400045.
Goertzel, B., and Pennachin, C. 2007. Artificial General Intelligence. Springer.
Koene, R. 2012a. Experimental Research in Whole Brain Emulation: The Need for Innovative In-Vivo Measurement Techniques. Special Issue of the International Journal
Kevin Gluck, Clayton Stanley, L. Moore, David Reitter and Marc Halbrügge
-441). New York: Cambridge University Press.
Knofcyznski, G. T., and Mundfrom, D. 2008. Sample sizes when using multiple linear regression for prediction. Educational and Psychological Measurement . 68:431-442.
Langley, P., Laird, J. E., and Rogers, S. 2008. Cognitive architectures: Research issues and challenges. Cognitive Systems Research, 10 (2), 141-160.
McClelland, J. L. 2009. The place of modeling in cognitive science. Topics in Cognitive Science, 1 , 11