Publications

PhD Thesis:

Books:

  1. Gagolewski M., Bartoszuk M., Cena A., Przetwarzanie i analiza danych w języku Python (Data Processing and Analysis in Python), Wydawnictwo Naukowe PWN, 2016, 369 pp. isbn:978-83-01-18940-2

Books translated:

  1. Will Kurt, Statystyka Bayesowska na wesoło (Bayesian Statistics the Fun Way), Wydawnictwo Naukowe PWN, 2020, 300 pp. isbn:978-83-01-21353-4

Articles in Journals:

  1. M. Gagolewski, M. Bartoszuk, A. Cena, Are cluster validity measures (in)valid?, Information Sciences 581, 620–636, 2021, doi:10.1016/j.ins.2021.10.004, url:https://github.com/gagolews/optim_cvi
  2. Bartoszuk M., Gagolewski M., T-norms or t-conorms? How to aggregate similarity degrees for plagiarism detection, Knowledge-Based Systems, 231, 2021, pp. 107427. doi:10.1016/j.knosys.2021.107427
  3. Bartoszuk M., Gagolewski M., SimilaR: R Code Clone and Plagiarism Detection, R Journal 12(1), 2020, pp. 367-385. doi:10.32614/RJ-2020-017
  4. Gagolewski M., Bartoszuk M., Cena A., Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm, Information Sciences 363, 2016, pp. 8-23. doi:10.1016/j.ins.2016.05.003

Papers in Edited Volumes and Proceedings:

  1. Bartoszuk M., Gagolewski M., Binary aggregation functions in software plagiarism detection, In: Proc. FUZZ-IEEE’17, IEEE, 2017, no. 8015582. doi:10.1109/FUZZ-IEEE.2017.8015582
  2. Gagolewski M., Cena A., Bartoszuk M., Hierarchical clustering via penalty-based aggregation and the Genie approach, In: Torra V. et al. (Eds.), Modeling Decisions for Artificial Intelligence (Lecture Notes in Artificial Intelligence 9880), Springer, 2016, pp. 191-202. doi:10.1007/978-3-319-45656-0_16
  3. Bartoszuk M., Beliakov G., Gagolewski M., James S., Fitting aggregation functions to data: Part I – Linearization and regularization, In: Carvalho J.P. et al. (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II (Communications in Computer and Information Science 611),
    Springer, 2016, pp. 767-779. doi:10.1007/978-3-319-40581-0_62
  4. Bartoszuk M., Beliakov G., Gagolewski M., James S., Fitting aggregation functions to data: Part II – Idempotentization, In: Carvalho J.P. et al. (Eds.),
    Information Processing and Management of Uncertainty in Knowledge-Based Systems,
    Part II (Communications in Computer and Information Science 611),
    Springer, 2016, pp. 780-789. doi:10.1007/978-3-319-40581-0_63
  5. Bartoszuk M., Gagolewski M., Detecting similarity of R functions via a
    fusion of multiple heuristic methods, In: Alonso J.M., Bustince H., Reformat
    M. (Eds.), Proc. IFSA/EUSFLAT 2015, Atlantis Press, 2015, pp. 419-426,
  6. Bartoszuk M., Solving systems of polynomial equations: a novel end
    condition and root computation method
    , In: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 543–552 (2014), doi:10.15439/2014F183,
  7. Bartoszuk M., Gagolewski M., A fuzzy R code similarity detection algorithm, In: Laurent A. et al. (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part III (CCIS 444), Springer-Verlag, Heidelberg, 2014, pp. 21-30.

Preprints and Research Reports:

  1. Bartoszuk M., Gagolewski M., Tuning up R code similarity detection algorithm, SRI PAS Research Report RB/10/2015.

Selected Talks:

  1. Detecting similarity of R functions via a fusion of multiple heuristic methods, The 16th World Congress of the International Fuzzy Systems Association and the 9th Conference of the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT), Gijón, Spain, June 30th – July 3rd, 2015
  2. Solving systems of polynomial equations: a novel end
    condition and root computation method
    , Federated Conference on Computer Science and Information Systems (FedCSIS), Warsaw, Poland, September 7 – 10, 2014
  3. A fuzzy R code similarity detection algorithm, 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), Montpellier, France, July 15 – 19, 2014

Miscellaneous:

  1. https://bezprawnik.pl/trzeci-sezon-black-mirror/
  2. Python — wyzwanie:
    1. https://kodolamacz.pl/blog/wyzwanie-python-1-hello-world/
    2. https://kodolamacz.pl/blog/wyzwanie-python-2-podstawowe-instrukcje/
    3. https://kodolamacz.pl/blog/wyzwanie-python-2-rozwiazanie/
    4. https://kodolamacz.pl/blog/wyzwanie-python-3-algorytmy-i-struktury-danych/
    5. https://kodolamacz.pl/blog/wyzwanie-python-3-rozwiazanie/
    6. https://kodolamacz.pl/blog/wyzwanie-python-4-programowanie-obiektowe/
    7. https://kodolamacz.pl/blog/wyzwanie-python-4-rozwiazanie/
    8. https://kodolamacz.pl/blog/wyzwanie-python-5-zaawansowane-aspekty-programowania-obiektowego/
    9. https://kodolamacz.pl/blog/wyzwanie-python-6-wyj%C4%85tki-oraz-operacje-na-plikach/
    10. https://kodolamacz.pl/blog/wyzwanie-python-6-rozwiazanie/
    11. https://kodolamacz.pl/blog/wyzwanie-python-7-web-scraping/
    12. https://kodolamacz.pl/blog/wyzwanie-python-7-rozwiazanie/
    13. https://kodolamacz.pl/bootcamp-python/
  3. https://nofluffjobs.com/blog/2018/01/31/jakie-kompetencje-powinien-posiadac-poczatkujacy-data-scientist/
  4. http://www.sages.com.pl/blog/data-scientisci-kim-sa-i-ile-zarabiaja-w-polsce-i-na-swiecie/
  5. http://blog.pclab.pl/cotuzgrzyta/Dziesi%C4%99%C4%87.najcz%C4%99%C5%9Bciej.pope%C5%82nianych.b%C5%82%C4%99d%C3%B3w.przez.pocz%C4%85tkuj%C4%85cych.Data.Scientist%C3%B3w,863
  6. Bartoszuk M., 3 najlepsze Python IDE dla Data Scientistów, magazyn Programista (8), 2017, s. 4-7
  7. http://www.r-bloggers.com/similar/, Blog post about SimilaR portal, part of my Ph.D. thesis.