skip to main content
10.1145/3437959.3459256acmconferencesArticle/Chapter ViewAbstractPublication PagespadsConference Proceedingsconference-collections
research-article

Comparing Implementations of Cellular Automata as Images: A Novel Approach to Verification by Combining Image Processing and Machine Learning

Published: 01 June 2021 Publication History

Abstract

Discrete models such as cellular automata may be ported from one platform or language onto another to improve performances, for instance by rewriting legacy Matlab code into C++ or adding optimizations into a Python implementation. Although such transformations can offer benefits such as scalability or maintainability, they also have the risk of introducing bugs. While standard verification techniques can always be applied, this situation presents a unique opportunity since the two implementations can be directly compared based on their simulation runs. Although comparing average results across runs of a same configuration is a common practice, our paper shows that many bugs would not be detected at this aggregate level. We thus propose comparing implementations of cellular automata by analyzing their outputs as images. In this paper, we examine the detection of several implementation errors using five different techniques (supervised/unsupervised image processing, decision trees, random forests, or deep learning) across three different cellular automata models (forest fire, tumor, HIV). We show that in some models, random forests can detect 4 out of 5 erroneous runs, although the accuracy depends both on the model and on the nature of the errors.

References

[1]
Adhyapok, P., Fu, X., Sluka, J. P., Clendenon, S. G., Sluka, V. D., Wang, Z., Dunn, K., Klaunig, J. E., and Glazier, J. A. A computational model of liver tissue damage and repair. PloS one 15, 12 (2020), e0243451.
[2]
Akar, Ö., and Güngör, O. Classification of multispectral images using random forest algorithm. Journal of Geodesy and Geoinformation 1, 2 (2012), 105--112.
[3]
Balci, O. Quality assessment, verification, and validation of modeling and simulation applications. In Proceedings of the 2004 Winter Simulation Conference (2004), vol. 1, Ieee.
[4]
Biau, G., and Scornet, E. A random forest guided tour. Test 25, 2 (2016), 197--227.
[5]
Blecic, I., Cecchini, A., and Trunfio, G. A. Cellular automata simulation of urban dynamics through gpgpu. The Journal of Supercomputing 65, 2 (2013), 614--629.
[6]
Canny, J. A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, 6 (1986), 679--698.
[7]
Cao, Y., Zhang, X., Fu, Y., Lu, Z., and Shen, X. Urban spatial growth modeling using logistic regression and cellular automata: A case study of hangzhou. Ecological Indicators 113 (2020), 106200.
[8]
Cho, K. Simple sparsification improves sparse denoising autoencoders in denoising highly corrupted images. In International conference on machine learning (2013), PMLR, pp. 432--440.
[9]
Dai, D., and Van Gool, L. Ensemble projection for semi-supervised image classification. In Proceedings of the IEEE International Conference on Computer Vision (2013), pp. 2072--2079.
[10]
Dai, J., Zhai, C., Ai, J., Ma, J., Wang, J., and Sun, W. Modeling the spread of epidemics based on cellular automata. Processes 9, 1 (2021), 55.
[11]
dos Santos, R. M. Z., and Coutinho, S. Dynamics of hiv infection: A cellular automata approach. Physical review letters 87, 16 (2001), 168102.
[12]
Encinas, L. H., White, S. H., del Rey, A. M., and Sánchez, G. R. Simulation of forest fire fronts using cellular automata. Advances in Engineering Software 38, 6 (2007), 372--378.
[13]
Fates, N. A guided tour of asynchronous cellular automata. In International Workshop on Cellular Automata and Discrete Complex Systems (2013), Springer, pp. 15--30.
[14]
Fisher, A., Adhikari, B., Zhai, C., Morgan, J. E., Mago, V. K., and Giabbanelli, P. J. Predicting the resource needs and outcomes of computationally intensive biological simulations. In 2020 Spring Simulation Conference (SpringSim) (2020), IEEE, pp. 1--12.
[15]
Fuchs, J., Fischer, F., Mansmann, F., Bertini, E., and Isenberg, P. Evaluation of alternative glyph designs for time series data in a small multiple setting. In Proceedings of the SIGCHI conference on human factors in computing systems (2013), pp. 3237--3246.
[16]
Gerlee, P., and Anderson, A. R. An evolutionary hybrid cellular automaton model of solid tumour growth. Journal of theoretical biology 246, 4 (2007), 583--603.
[17]
Giabbanelli, P. J. Solving challenges at the interface of simulation and big data using machine learning. In 2019 Winter Simulation Conference (WSC) (2019), IEEE, pp. 572--583.
[18]
Giabbanelli, P. J., Babu, G. J., and Baniukiewicz, M. A novel visualization environment to support modelers in analyzing data generated by cellular automata. In International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (2016), Springer, pp. 529--540.
[19]
Giabbanelli, P. J., Devita, J. A., Köster, T., and Kohrt, J. A. Optimizing discrete simulations of the spread of hiv-1 to handle billions of cells on a workstation. In Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (2020), pp. 67--78.
[20]
Giabbanelli, P. J., Freeman, C., Devita, J. A., Rosso, N., and Brumme, Z. L. Mechanisms for cell-to-cell and cell-free spread of hiv-1 in cellular automata models. In Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (2019), pp. 103--114.
[21]
Giordano, A., De Rango, A., Rongo, R., D'Ambrosio, D., and Spataro, W. Dynamic load balancing in parallel execution of cellular automata. IEEE Transactions on Parallel and Distributed Systems 32, 2 (2020), 470--484.
[22]
Gondara, L. Medical image denoising using convolutional denoising autoencoders. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) (2016), IEEE, pp. 241--246.
[23]
Gonzalez, R. C., Woods, R. E., and Masters, B. R. Digital image processing third edition. Pearson Prentice Hall (2008), 743--747.
[24]
Grantham, E. O., and Giabbanelli, P. J. Creating perceptual uncertainty in agent-based models with social interactions. In 2020 Spring Simulation Conference (SpringSim) (2020), IEEE, pp. 1--12.
[25]
Gutierrez, A., Beckmann, B. M., Dutu, A., Gross, J., LeBeane, M., Kalamatianos, J., Kayiran, O., Poremba, M., Potter, B., Puthoor, S., et al. Lost in abstraction: Pitfalls of analyzing GPUs at the intermediate language level. In 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA) (2018), IEEE, pp. 608--619.
[26]
Hadavi, N., Nordin, M. J., and Shojaeipour, A. Lung cancer diagnosis using ct-scan images based on cellular learning automata. In 2014 International Conference on Computer and Information Sciences (ICCOINS) (2014), IEEE, pp. 1--5.
[27]
Hardasmal, A. J. T., and Salguero, A. G. Teaching parallelism with gamification in cellular automaton environments. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 15, 1 (2020), 34--42.
[28]
Hassanijalilian, O., Igathinathane, C., Bajwa, S., and Nowatzki, J. Rating iron deficiency in soybean using image processing and decision-tree based models. Remote Sensing 12, 24 (2020), 4143.
[29]
Hatzikirou, H., Breier, G., and Deutsch, A. Cellular automaton modeling of tumor invasion. Complex Social and Behavioral Systems: Game Theory and Agent-Based Models (2020), 851--863.
[30]
He, K., Zhang, X., Ren, S., and Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (2016), pp. 770--778.
[31]
Hecht-Nielsen, R. Theory of the backpropagation neural network. In Neural networks for perception. Elsevier, 1992, pp. 65--93.
[32]
Hillmann, A., Crane, M., and Ruskin, H. J. Assessing the impact of hiv treatment interruptions using stochastic cellular automata. Journal of theoretical biology 502 (2020), 110376.
[33]
Im, J., and Jensen, J. R. A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sensing of Environment 99, 3 (2005), 326--340.
[34]
Janssen, M. A. The practice of archiving model code of agent-based models. Journal of Artificial Societies and Social Simulation 20, 1 (2017).
[35]
Jiang, W., Wang, F., Fang, L., Zheng, X., Qiao, X., Li, Z., and Meng, Q. Modelling of wildland-urban interface fire spread with the heterogeneous cellular automata model. Environmental Modelling & Software 135 (2021), 104895.
[36]
Karafyllidis, I., and Thanailakis, A. A model for predicting forest fire spreading using cellular automata. Ecological Modelling 99, 1 (1997), 87--97.
[37]
Köster, T., Giabbanelli, P. J., and Uhrmacher, A. M. Performance and soundness of simulation: A case study based on a cellular automaton for in-body spread of HIV . In Winter Simulation Conference (WSC 2020) (2020).
[38]
Kouzani, A., Nahavandi, S., and Khoshmanesh, K. Face classification by a random forest. In TENCON 2007--2007 IEEE Region 10 Conference (2007), IEEE, pp. 1--4.
[39]
Li, C., Li, J., Hu, L., and Hou, D. Visualization and simulation model of underground mine fire disaster based on cellular automata. Applied Mathematical Modelling 39, 15 (2015), 4351--4364.
[40]
Li, Y., Chen, M., Dou, Z., Zheng, X., Cheng, Y., and Mebarki, A. A review of cellular automata models for crowd evacuation. Physica A: Statistical Mechanics and its Applications 526 (2019), 120752.
[41]
Lilly, H. A. The use of cellular automata in the classroom. In Supercomputing'95: Proceedings of the 1995 ACM/IEEE Conference on Supercomputing (1995), IEEE, pp. 16--16.
[42]
Lindenbaum, M., Fischer, M., and Bruckstein, A. On gabor's contribution to image enhancement. Pattern recognition 27, 1 (1994), 1--8.
[43]
Liu, Y., Guo, J., Taplin, J., and Wang, Y. Characteristic analysis of mixed traffic flow of regular and autonomous vehicles using cellular automata. Journal of Advanced Transportation 2017 (2017).
[44]
Maimon, O. Z., and Rokach, L. Data mining with decision trees: theory and applications, vol. 81. World scientific, 2014.
[45]
Mhaskar, H. N., and Micchelli, C. A. How to choose an activation function. In Advances in Neural Information Processing Systems (1994), pp. 319--326.
[46]
Monteagudo, Á., and Santos, J. Treatment analysis in a cancer stem cell context using a tumor growth model based on cellular automata. PloS one 10, 7 (2015), e0132306.
[47]
Nakagawa, Y., and Rosenfeld, A. Some experiments on variable thresholding. Pattern recognition 11, 3 (1979), 191--204.
[48]
Otsu, N. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics 9, 1 (1979), 62--66.
[49]
Owen, D. C., Bensi, M. T., Davis, A. P., and Aydilek, A. H. Measuring soil coverage using image feature descriptors and the decision tree learning algorithm. Biosystems Engineering 196 (2020), 112--126.
[50]
Pal, M. Random forest classifier for remote sensing classification. International journal of remote sensing 26, 1 (2005), 217--222.
[51]
Pan, S. J., and Yang, Q. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2009), 1345--1359.
[52]
Papaccio, F., Paino, F., Regad, T., Papaccio, G., Desiderio, V., and Tirino, V. Concise review: cancer cells, cancer stem cells, and mesenchymal stem cells: influence in cancer development. Stem cells translational medicine 6, 12 (2017), 2115--2125.
[53]
Patel, A. A., Gawlinski, E. T., Lemieux, S. K., and Gatenby, R. A. A cellular automaton model of early tumor growth and invasion: the effects of native tissue vascularity and increased anaerobic tumor metabolism. Journal of theoretical biology 213, 3 (2001), 315--331.
[54]
Pedamonti, D. Comparison of non-linear activation functions for deep neural networks on mnist classification task. arXiv preprint arXiv:1804.02763 (2018).
[55]
Polan, D. F., Brady, S. L., and Kaufman, R. A. Tissue segmentation of computed tomography images using a random forest algorithm: a feasibility study. Physics in Medicine & Biology 61, 17 (2016), 6553.
[56]
Poleszczuk, J., and Enderling, H. A high-performance cellular automaton model of tumor growth with dynamically growing domains. Applied mathematics 5, 1 (2014), 144.
[57]
Pourhasanzade, F., and Sabzpoushan, S. A cellular automata model of chemotherapy effects on tumour growth: targeting cancer and immune cells. Mathematical and Computer Modelling of Dynamical Systems 25, 1 (2019), 63--89.
[58]
Precharattana, M. Stochastic modeling for dynamics of hiv-1 infection using cellular automata: A review. Journal of bioinformatics and computational biology 14, 01 (2016), 1630001.
[59]
Prewitt, J. M. Object enhancement and extraction. Picture processing and Psychopictorics 10, 1 (1970), 15--19.
[60]
Qi, A.-S., Zheng, X., Du, C.-Y., and An, B.-S. A cellular automaton model of cancerous growth. Journal of theoretical biology 161, 1 (1993), 1--12.
[61]
Rana, E., Giabbanelli, P. J., Balabhadrapathruni, N. H., Li, X., and Mago, V. K. Exploring the relationship between adherence to treatment and viral load through a new discrete simulation model of hiv infectivity. In Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (2015), pp. 145--156.
[62]
Ribba, B., Alarcón, T., Marron, K., Maini, P. K., and Agur, Z. The use of hybrid cellular automaton models for improving cancer therapy. In International Conference on Cellular Automata (2004), Springer, pp. 444--453.
[63]
Rosin, P., Adamatzky, A., and Sun, X. Cellular automata in image processing and geometry. Springer, 2014.
[64]
Rosso, N., and Giabbanelli, P. Accurately inferring compliance to five major food guidelines through simplified surveys: applying data mining to the uk national diet and nutrition survey. JMIR public health and surveillance 4, 2 (2018), e56.
[65]
Rubio, Y., Montiel, O., and Sepúlveda, R. Microcalcification detection in mammograms based on fuzzy logic and cellular automata. In Nature-Inspired Design of Hybrid Intelligent Systems. Springer, 2017, pp. 583--602.
[66]
Salguero, A. G., Capel, M. I., and Tomeu, A. J. Parallel cellular automaton tumor growth model. In International Conference on Practical Applications of Computational Biology & Bioinformatics (2018), Springer, pp. 175--182.
[67]
Sanchez, S. M. Data farming: Methods for the present, opportunities for the future. ACM Transactions on Modeling and Computer Simulation (TOMACS) 30, 4 (2020), 1--30.
[68]
Senatore, A. a. Accelerating a three-dimensional eco-hydrological cellular automaton on gpgpu with opencl. In AIP Conference Proceedings (2016), vol. 1776, AIP Publishing LLC, p. 080003.
[69]
Shorten, C., Khoshgoftaar, T. M., and Furht, B. Deep learning applications for covid-19. Journal of Big Data 8, 1 (2021), 1--54.
[70]
Shu, D., et al. Modeling of dynamic recrystallization behavior of as-extruded am50 magnesium alloy during hot compression by a cellular automaton method. Metals 11, 1 (2021), 75.
[71]
Sobel, I., and Feldman, G. An isotropic 3x3 image gradient operator. presentation at stanford ai project, 1968.
[72]
Solihin, Y., and Leedham, C. Integral ratio: a new class of global thresholding techniques for handwriting images. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 8 (1999), 761--768.
[73]
Staubitz, T., Teusner, R., Meinel, C., and Prakash, N. Cellular automata as basis for programming exercises in a mooc on test driven development. In 2016 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) (2016), IEEE, pp. 374--380.
[74]
Su, Y., et al. Diagnosis of gastric cancer using decision tree classification of mass spectral data. Cancer science 98, 1 (2007), 37--43.
[75]
Suzuki, S., et al. Topological structural analysis of digitized binary images by border following. Computer vision, graphics, and image processing 30, 1 (1985), 32--46.
[76]
Vincent, P., et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research 11, 12 (2010).
[77]
Voinov, A., et al. Tools and methods in participatory modeling: Selecting the right tool for the job. Environmental Modelling & Software 109 (2018), 232--255.
[78]
Wang, X.-F., Du, J.-X., and Zhang, G.-J. Recognition of leaf images based on shape features using a hypersphere classifier. In International Conference on Intelligent Computing (2005), Springer, pp. 87--96.
[79]
Zhang, B. Reliable classification of vehicle types based on cascade classifier ensembles. IEEE Transactions on intelligent transportation systems 14, 1 (2012), 322--332.
[80]
Zhang, C., et al. Resnet or densenet? introducing dense shortcuts to resnet. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (2021), pp. 3550--3559.

Cited By

View all
  • (2024)Experimental evaluation of a machine learning approach to improve the reproducibility of network simulationsSimulation10.1177/00375497241229753100:6(545-561)Online publication date: 21-Jun-2024
  • (2024)Tumor growth for remodeling process: A 2D approachJournal of Theoretical Biology10.1016/j.jtbi.2024.111781585(111781)Online publication date: May-2024
  • (2022)An Empirical Study on the Training Characteristics of Weekly Load of Calisthenics Teaching and Training Based on Deep Learning AlgorithmScientific Programming10.1155/2022/51590272022Online publication date: 1-Jan-2022
  • Show More Cited By

Index Terms

  1. Comparing Implementations of Cellular Automata as Images: A Novel Approach to Verification by Combining Image Processing and Machine Learning

          Recommendations

          Comments

          Information & Contributors

          Information

          Published In

          cover image ACM Conferences
          SIGSIM-PADS '21: Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
          May 2021
          181 pages
          ISBN:9781450382960
          DOI:10.1145/3437959
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Sponsors

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 01 June 2021

          Permissions

          Request permissions for this article.

          Check for updates

          Author Tags

          1. HIV
          2. cellular automata
          3. forest fire
          4. image processing
          5. tumor
          6. verification

          Qualifiers

          • Research-article

          Conference

          SIGSIM-PADS '21
          Sponsor:

          Acceptance Rates

          Overall Acceptance Rate 398 of 779 submissions, 51%

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • Downloads (Last 12 months)18
          • Downloads (Last 6 weeks)1
          Reflects downloads up to 14 Feb 2025

          Other Metrics

          Citations

          Cited By

          View all
          • (2024)Experimental evaluation of a machine learning approach to improve the reproducibility of network simulationsSimulation10.1177/00375497241229753100:6(545-561)Online publication date: 21-Jun-2024
          • (2024)Tumor growth for remodeling process: A 2D approachJournal of Theoretical Biology10.1016/j.jtbi.2024.111781585(111781)Online publication date: May-2024
          • (2022)An Empirical Study on the Training Characteristics of Weekly Load of Calisthenics Teaching and Training Based on Deep Learning AlgorithmScientific Programming10.1155/2022/51590272022Online publication date: 1-Jan-2022
          • (2022)A New Application of Machine Learning: Detecting Errors in Network Simulations2022 Winter Simulation Conference (WSC)10.1109/WSC57314.2022.10015484(653-664)Online publication date: 11-Dec-2022
          • (2021)When Do We Need Massive Computations to Perform Detailed COVID‐19 Simulations?Advanced Theory and Simulations10.1002/adts.2021003435:2Online publication date: 23-Nov-2021

          View Options

          Login options

          View options

          PDF

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          Figures

          Tables

          Media

          Share

          Share

          Share this Publication link

          Share on social media