{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:17:28Z","timestamp":1761175048594,"version":"build-2065373602"},"reference-count":30,"publisher":"Polish Information Processing Society","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"DOI":"10.15439\/2025f1666","type":"proceedings-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:44:23Z","timestamp":1761119063000},"page":"219-230","source":"Crossref","is-referenced-by-count":0,"title":["Deep Differentiable Logic Gate Networks Based on Fuzzy \u0141ukasiewicz T-norm"],"prefix":"10.15439","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1339-9841","authenticated-orcid":true,"given":"Chan Duong","family":"Nguy","sequence":"first","affiliation":[{"name":"Vistula University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0027-1102","authenticated-orcid":true,"given":"Piotr","family":"Wasilewski","sequence":"additional","affiliation":[{"name":"Polish Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"6175","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","unstructured":"B. Becker and R. Kohavi, \u201cAdult,\" UCI Machine Learning Repository,\n1996. Available: https:\/\/doi.org\/10.24432\/C5XW20","DOI":"10.24432\/C5XW20"},{"key":"ref2","doi-asserted-by":"publisher","unstructured":"S. Bosse, \u201cIoT and Edge Computing using virtualized low-resource\ninteger Machine Learning with support for CNN, ANN, and Decision\nTrees,\u201d Proc. 18th Conf. Comput. Sci. Intell. Syst. (FedCSIS), vol. 35,\npp. 367\u2013376, 2023, M. Ganzha, L. Maciaszek, M. Paprzycki, and D.\n\u015al\u02db ezak, Eds., IEEE. Available: http:\/\/dx.doi.org\/10.15439\/2023F7745","DOI":"10.15439\/2023F7745"},{"key":"ref3","unstructured":"J. Choi, Z. Wang, S. Venkataramani, P. Chuang, V. Srinivasan, and K.\nGopalakrishnan, \u201cPACT: Parameterized Clipping Activation for Quantized Neural Networks,\" 2018. Available: https:\/\/arxiv.org\/abs\/1805.06085"},{"key":"ref4","doi-asserted-by":"publisher","unstructured":"L. da Cruz, C. Sierra-Franco, G. Silva-Calpa, and A. Raposo, \u201cEnabling\nAutonomous Medical Image Data Annotation: A human-in-the-loop\nReinforcement Learning Approach,\u201d in *Proc. 16th Conf. on Computer\nScience and Intelligence Systems (FedCSIS)*, vol. 25, M. Ganzha, L.\nMaciaszek, M. Paprzycki, and D. \u015al\u02db ezak, Eds., IEEE, 2021, pp. 271\u2013279. Available: http:\/\/dx.doi.org\/10.15439\/2021F86","DOI":"10.15439\/2021F86"},{"key":"ref5","doi-asserted-by":"publisher","unstructured":"L. Dey, S. Jana, T. Dasgupta, and T. Gupta, \u201cDeciphering Clinical\nNarratives \u2013 Augmented Intelligence for Decision Making in Healthcare\nSector,\u201d in *Proc. 18th Conf. on Computer Science and Intelligence\nSystems*, M. Ganzha, L. Maciaszek, M. Paprzycki, and D. \u015al\u02db ezak, Eds.,\nvol. 35, *Annals of Computer Science and Information Systems*, IEEE,\n2023, pp. 11\u201324. Available: http:\/\/dx.doi.org\/10.15439\/2023F3385","DOI":"10.15439\/2023F3385"},{"key":"ref6","doi-asserted-by":"publisher","unstructured":"D. D\u0142ugosz, A. Kr\u00f3lak, T. Eftest\u00f8l, S. \u00d8rn, T. Wiktorski, K. R. J. Oskal,\nand M. Nyg\u00e5rd, \u201cECG Signal Analysis for Troponin Level Assessment\nand Coronary Artery Disease Detection: the NEEDED Study 2014,\u201d\nin *Proc. 2018 Federated Conf. Comput. Sci. Inf. Syst.*, vol. 15, M.\nGanzha, L. Maciaszek, and M. Paprzycki, Eds. IEEE, 2018, pp. 1065\u20131068. Available: http:\/\/dx.doi.org\/10.15439\/2018F247","DOI":"10.15439\/2018F247"},{"key":"ref7","unstructured":"S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan, \u201cDeep\nlearning with limited numerical precision,\" in Proceedings of the\n32nd International Conference on Machine Learning (ICML\u201915), 2015,\npp. 1737\u20131746."},{"key":"ref8","unstructured":"T. Hoefler, D. Alistarh, T. Ben-Nun, N. Dryden, and A. Peste, \u201cSparsity\nin deep learning: pruning and growth for efficient inference and training\nin neural networks,\" J. Mach. Learn. Res., vol. 22, no. 1, art. no. 241,\nJan. 2021, 124 pp."},{"key":"ref9","unstructured":"D. P. Kingma and J. Ba, \u201cAdam: A Method for Stochastic Optimization,\"\n2014. Available: https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"E. van Krieken, E. Acar, and F. van Harmelen, \u201cAnalyzing Differentiable Fuzzy Logic Operators,\" Artificial Intelligence, vol. 302, 2022,\npp. 103602. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0004370221001533","DOI":"10.1016\/j.artint.2021.103602"},{"key":"ref11","doi-asserted-by":"publisher","unstructured":"J. Kosi\u0144ski, K. Szklanny, A. Wieczorkowska, and M. Wichrowski, \u201cAn\nAnalysis of Game-Related Emotions Using EMOTIV EPOC,\u201d *Proc.\n2018 Federated Conf. on Computer Science and Information Systems\n(FedCSIS)*, vol. 15, Annals of Computer Science and Information Systems, pp. 913\u2013917, 2018, M. Ganzha, L. Maciaszek, and M. Paprzycki,\nEds. IEEE. Available: http:\/\/dx.doi.org\/10.15439\/2018F296","DOI":"10.15439\/2018F296"},{"key":"ref12","unstructured":"A. Krizhevsky, \u201cLearning Multiple Layers of Features from Tiny Images,\" Univ. of Toronto, 2012."},{"key":"ref13","unstructured":"Y. LeCun, C. Cortes, and C. J. C. Burges, \u201cThe MNIST database of\nhandwritten digits,\" 1998. Available: http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"ref14","doi-asserted-by":"publisher","unstructured":"M. Marcinkiewicz and G. Mrukwa, \u201cQuantitative Impact of Label Noise\non the Quality of Segmentation of Brain Tumors on MRI scans,\u201d\nProc. 2019 Federated Conf. on Computer Science and Information\nSystems (FedCSIS), vol. 18, pp. 61\u201365, 2019. Edited by M. Ganzha,\nL. Maciaszek, and M. Paprzycki. IEEE. Available: http:\/\/dx.doi.org\/10.15439\/2019F273","DOI":"10.15439\/2019F273"},{"key":"ref15","doi-asserted-by":"publisher","unstructured":"K. Menger, \u201cStatistical Metrics,\" Proc. Nat. Acad. Sci. U.S.A., vol. 28,\nno. 12, Dec. 1942, pp. 535\u2013537. Available: https:\/\/doi.org\/10.1073\/pnas.28.12.535","DOI":"10.1073\/pnas.28.12.535"},{"key":"ref16","doi-asserted-by":"publisher","unstructured":"D. C. Mocanu, E. Mocanu, P. Stone, P. H. Nguyen, M. Gibescu, and\nA. Liotta, \u201cScalable training of artificial neural networks with adaptive\nsparse connectivity inspired by network science,\" Nature Communications, vol. 9, no. 1, Jun. 2018, art. 2383. Available: https:\/\/doi.org\/10.1038\/s41467-018-04316-3","DOI":"10.1038\/s41467-018-04316-3"},{"key":"ref17","doi-asserted-by":"publisher","unstructured":"A. Morar, F. Moldoveanu, A. Moldoveanu, O. Balan, and V. Asavei,\n\u201cGPU Accelerated 2D and 3D Image Processing,\u201d in *Proc. 2017\nFederated Conf. Comput. Sci. Inf. Syst. (FedCSIS)*, vol. 11, M. Ganzha,\nL. Maciaszek, and M. Paprzycki, Eds., IEEE, 2017, pp. 653\u2013656.\nAvailable: http:\/\/dx.doi.org\/10.15439\/2017F265","DOI":"10.15439\/2017F265"},{"key":"ref18","unstructured":"A. Paszke et al., \u201cPyTorch: An Imperative Style, High-Performance Deep\nLearning Library,\" 2019. Available: https:\/\/arxiv.org\/abs\/1912.01703"},{"key":"ref19","unstructured":"F. Petersen, C. Borgelt, H. Kuehne, and O. Deussen, \u201cDeep\nDifferentiable Logic Gate Networks,\" in Advances in Neural\nInformation Processing Systems, vol. 35, 2022, pp. 2006\u20132018.\nAvailable:\n https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/0d3496dd0cec77a999c98d35003203ca-Paper-Conference.pdf"},{"key":"ref20","unstructured":"F. Petersen, H. Kuehne, C. Borgelt, J. Welzel, and S. Ermon,\n\u201cConvolutional Differentiable Logic Gate Networks,\" in Advances in\nNeural Information Processing Systems, vol. 37, 2024, pp. 121185\u2013121203. Available: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/db988b089d8d97d0f159c15ed0be6a71-Paper-Conference.pdf"},{"key":"ref21","doi-asserted-by":"publisher","unstructured":"M. Pudo, M. Wosik, and A. Janicki, \u201cOpen Vocabulary Keyword\nSpotting with Small-Footprint ASR-based Architecture and Language\nModels,\u201d *Proc. 18th Conf. Computer Science and Intelligence Systems\n(FedCSIS)*, Annals of Computer Science and Information Systems,\nvol. 35, pp. 657\u2013666, 2023, M. Ganzha, L. Maciaszek, M. Paprzycki, and\nD. \u015al\u02db ezak, Eds., IEEE. Available: http:\/\/dx.doi.org\/10.15439\/2023F8594","DOI":"10.15439\/2023F8594"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"H. Qin, R. Gong, X. Liu, X. Bai, J. Song, and N. Sebe, \u201cBinary\nneural networks: A survey,\" Pattern Recognition, vol. 105, 2020,\nart. 107281. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0031320320300856","DOI":"10.1016\/j.patcog.2020.107281"},{"key":"ref23","doi-asserted-by":"publisher","unstructured":"M. Rapoport and T. Tamir, \u201cBest Response Dynamics for VLSI Physical\nDesign Placement,\u201d in *Proc. 2019 Federated Conf. on Computer Science and Information Systems (FedCSIS)*, M. Ganzha, L. Maciaszek,\nand M. Paprzycki, Eds., IEEE, vol. 18, 2019, pp. 147\u2013156. Available:\nhttp:\/\/dx.doi.org\/10.15439\/2019F91","DOI":"10.15439\/2019F91"},{"key":"ref24","doi-asserted-by":"publisher","unstructured":"D. E. Rumelhart, G. E. Hinton, and R. J. Williams, \u201cLearning representations by back-propagating errors,\" Nature, vol. 323, no. 6088, Oct.\n1986, pp. 533\u2013536. Available: https:\/\/doi.org\/10.1038\/323533a0","DOI":"10.1038\/323533a0"},{"key":"ref25","doi-asserted-by":"publisher","unstructured":"A. Telikani, A. Tahmassebi, W. Banzhaf, and A. H. Gandomi, \u201cEvolutionary Machine Learning: A Survey,\" ACM Comput. Surv., vol. 54,\nno. 8, art. 161, Oct. 2021. Available: https:\/\/doi.org\/10.1145\/3467477","DOI":"10.1145\/3467477"},{"key":"ref26","unstructured":"S. Thrun et al.,\u201cThe Monk\u2019s Problem\u2019s: A Performance Comparison of Different Learning Methods,\" 1991. Available: https:\/\/api.semanticscholar.org\/CorpusID:59810521"},{"key":"ref27","doi-asserted-by":"publisher","unstructured":"L. Uberg and S. Kadry, \u201cAnalysis of Brain Tumor Using MRI Images,\u201d\nin *Proc. 17th Conf. Computer Science and Intelligence Systems (FedCSIS)*, M. Ganzha, L. Maciaszek, M. Paprzycki, and D. \u015al\u02db ezak, Eds.,\nvol. 30, *Annals of Computer Science and Information Systems*, IEEE,\n2022, pp. 201\u2013204. Available: http:\/\/dx.doi.org\/10.15439\/2022F69","DOI":"10.15439\/2022F69"},{"key":"ref28","unstructured":"P. Wasilewski, and Ch. D. Nguy, \u201cDeep Differentiable Logic Gate\nNetworks Based on Fuzzy Zadeh\u2019s T-norm,\u201d Proceedings of 6th Polish\nConference on Artificial Intelligence (PP-RAI 2025), Katowice, Poland,\nApr. 7-9, 2025. to appear in Lecture Notes in Networks and Systems,\nSpringer."},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"L. A. Zadeh, \u201cFuzzy sets,\" Information and Control, vol. 8, no. 3, 1965,\npp. 338\u2013353. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S001999586590241X","DOI":"10.1016\/S0019-9958(65)90241-X"},{"key":"ref30","doi-asserted-by":"publisher","unstructured":"M. Zwitter and M. Soklic, \u201cBreast Cancer,\" UCI Machine Learning\nRepository, 1988. Available: https:\/\/doi.org\/10.24432\/C51P4M","DOI":"10.24432\/C51P4M"}],"event":{"name":"20th Conference on Computer Science and Intelligence Systems (FedCSIS)","theme":"Computer Science and Intelligence Systems","location":"Krak\u00f3w, Poland","acronym":"FedCSIS","number":"20","start":{"date-parts":[[2025,9,14]]},"end":{"date-parts":[[2025,9,17]]}},"container-title":["Annals of Computer Science and Information Systems","Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)"],"original-title":[],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:46:21Z","timestamp":1761119181000},"score":1,"resource":{"primary":{"URL":"https:\/\/annals-csis.org\/Volume_43\/drp\/1666.html"}},"subtitle":[],"proceedings-subject":"Computer Science and Information Systems","short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":30,"URL":"https:\/\/doi.org\/10.15439\/2025f1666","relation":{},"ISSN":["2300-5963"],"issn-type":[{"value":"2300-5963","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}