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 ===== Papers =====
-  * [1] V. Rodriguez-Fernandez, A. Trzcionkowska, A. Gonzalez-Pardo, E. Brzychczy, G. J. Nalepa, and D. Camacho. Conformance Checking for Time Series-aware Processes. IEEE Transactions on Industrial Informatics. 17(2): 871-881 (2021)
+  * [1] V. Rodriguez-Fernandez, A. Trzcionkowska, A. Gonzalez-Pardo, E. Brzychczy, G. J. Nalepa, and D. Camacho. Conformance Checking for Time Series-aware Processes. IEEE Transactions on Industrial Informatics. 17(2): 871-881 (2021) https://doi.org/10.1109/TII.2020.2977126
-  * [2] D. Calvanese, S. Ghilardi, A. Gianola, M. Montali, and A. Rivkin. SMT-based Verification of Data-Aware Processes: A Model-Theoretic Approach. Mathematical Structures in Computer Science. 2020. 30(3): 271-313 (2020)
+  * [2] D. Calvanese, S. Ghilardi, A. Gianola, M. Montali, and A. Rivkin. SMT-based Verification of Data-Aware Processes: A Model-Theoretic Approach. Mathematical Structures in Computer Science. 2020. 30(3): 271-313 (2020) https://doi.org/10.1017/S0960129520000067
-  * [3] M. Szpyrka, E. Brzychczy, A. Napieraj, J. Korski, G. J. Nalepa, Conformance Checking of a Longwall Shearer Operation Based on Low-Level Events, Energies 2020, 13, 6630.
+  * [3] M. Szpyrka, E. Brzychczy, A. Napieraj, J. Korski, G. J. Nalepa, Conformance Checking of a Longwall Shearer Operation Based on Low-Level Events, Energies 2020, 13, 6630. https://doi.org/10.3390/en13246630
-  * [4] M. Szelążek, S. Bobek, A. Gonzalez-Pardo, G. J. Nalepa, “Towards the Modeling of the Hot Rolling Industrial Process. Preliminary Results”. In: International Conference on Intelligent Data Engineering and Automated Learning (pp. 385-396). Springer, 2020.
+  * [4] M. Szelążek, S. Bobek, A. Gonzalez-Pardo, G. J. Nalepa, “Towards the Modeling of the Hot Rolling Industrial Process. Preliminary Results”. In: International Conference on Intelligent Data Engineering and Automated Learning (pp. 385-396). Springer, 2020. https://doi.org/10.1007/978-3-030-62362-3_34
-  * [5] S. Bobek, A. Trzcinkowska, E. Brzychczy, G. J. Nalepa: Cluster Discovery from Sensor Data Incorporating Expert Knowledge, Workshop of Knowledge Representation & Representation Learning KR4L, ECAI 2020 in Santiago de Compostela, June 2020 https://smartdataanalytics.github.io/KR4L
+  * [5] S. Bobek, A. Trzcinkowska, E. Brzychczy, G. J. Nalepa: Cluster Discovery from Sensor Data Incorporating Expert Knowledge, Workshop of Knowledge Representation & Representation Learning KR4L, ECAI 2020 in Santiago de Compostela, June 2020 https://smartdataanalytics.github.io/KR4L http://ceur-ws.org/Vol-3020/KR4L_paper_5.pdf
-  * [6] S. Bobek and G. J. Nalepa. Augmenting automatic clustering with expert knowledge and explanations. In Computational Science – ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part IV, page 631–638, Cham, 2021. Springer International Publishing
+  * [6] S. Bobek and G. J. Nalepa. Augmenting automatic clustering with expert knowledge and explanations. In Computational Science – ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part IV, page 631–638, Cham, 2021. Springer International Publishing https://doi.org/10.1007/978-3-030-77970-2_48
-  * [7] S. Bobek, M. Mozolewski, and G. J. Nalepa. Explanation-driven model stacking. In M. Paszynski, D. Kranzlmüller, V. V. Krzhizhanovskaya, J. J. Dongarra, and P. M. A. Sloot, editors, Computational Science – ICCS 2021, pages 361–371, Cham, 2021. Springer International Publishing.
+  * [7] S. Bobek, M. Mozolewski, and G. J. Nalepa. Explanation-driven model stacking. In M. Paszynski, D. Kranzlmüller, V. V. Krzhizhanovskaya, J. J. Dongarra, and P. M. A. Sloot, editors, Computational Science – ICCS 2021, pages 361–371, Cham, 2021. Springer International Publishing. https://doi.org/10.1007/978-3-030-77980-1_28
-  * [8] F. Piccialli, F. Giampaolo, E. Prezioso, D. Camacho, and G. Acampora, “Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion,” Information Fusion, vol. 74, pp. 1–16, Oct. 2021
+  * [8] F. Piccialli, F. Giampaolo, E. Prezioso, D. Camacho, and G. Acampora, “Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion,” Information Fusion, vol. 74, pp. 1–16, Oct. 2021 https://doi.org/10.1016/j.inffus.2021.03.004
-  * [9] H. Liz, M. Sánchez-Montañés, A. Tagarro, S. Domínguez-Rodríguez, R. Dagan, and D. Camacho, “Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis,” Future Generation Computer Systems, vol. 122, pp. 220–233, Sep. 2021.
+  * [9] H. Liz, M. Sánchez-Montañés, A. Tagarro, S. Domínguez-Rodríguez, R. Dagan, and D. Camacho, “Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis,” Future Generation Computer Systems, vol. 122, pp. 220–233, Sep. 2021. https://doi.org/10.1016/j.future.2021.04.007
-  * [10] J. Huertas-Tato, A. Martín, J. Fierrez, and D. Camacho, “Fusing CNNs and statistical indicators to improve image classification,” Information Fusion, vol. 79, pp. 174–187, Mar. 2022.
+  * [10] J. Huertas-Tato, A. Martín, J. Fierrez, and D. Camacho, “Fusing CNNs and statistical indicators to improve image classification,” Information Fusion, vol. 79, pp. 174–187, Mar. 2022. https://doi.org/10.1016/j.inffus.2021.09.012
-  * [11] A. I. Torre-Bastida, J. Díaz-de-Arcaya, E. Osaba, K. Muhammad, D. Camacho, and J. Del Ser, “Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions,” Neural Comput & Applic, Aug. 2021.
+  * [11] A. I. Torre-Bastida, J. Díaz-de-Arcaya, E. Osaba, K. Muhammad, D. Camacho, and J. Del Ser, “Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions,” Neural Comput & Applic, Aug. 2021. https://doi.org/10.1007/s00521-021-06332-9
-  * [12] S. Bobek, S. K. Tadeja, Struski, P. Stachura, T. Kipouros, J. Tabor, G. J. Nalepa, and P. O. Kristensson. “Virtual reality-based parallel coordinates plots enhanced with explainable AI and data-science analytics for decision-making processes.” Applied Sciences, 12(1), 2022
+  * [12] S. Bobek, S. K. Tadeja, Struski, P. Stachura, T. Kipouros, J. Tabor, G. J. Nalepa, and P. O. Kristensson. “Virtual reality-based parallel coordinates plots enhanced with explainable AI and data-science analytics for decision-making processes.” Applied Sciences, 12(1), 2022 https://doi.org/10.3390/app12010331
-  * [13] G. J. Nalepa, S. Bobek, K. Kutt, and M. Atzmueller. “Semantic data mining in ubiquitous sensing: A survey.” Sensors, 21(13), 2021.
+  * [13] G. J. Nalepa, S. Bobek, K. Kutt, and M. Atzmueller. “Semantic data mining in ubiquitous sensing: A survey.” Sensors, 21(13), 2021. https://doi.org/10.3390/s21134322
-  * [14] M. Kuk, S. Bobek and G. J. Nalepa, "Explainable clustering with multidimensional bounding boxes," 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 2021, pp. 1-10.
+  * [14] M. Kuk, S. Bobek and G. J. Nalepa, "Explainable clustering with multidimensional bounding boxes," 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 2021, pp. 1-10. https://doi.org/10.1109/DSAA53316.2021.9564220
-  * [15] S. Bobek, M. Kuk, J. Brzegowski, E. Brzychczy, and G. J. Nalepa. “KnAC: an approach for enhancing cluster analysis with background knowledge and explanations.” CoRR, abs/2112.08759, 2021
+  * [15] S. Bobek, M. Kuk, J. Brzegowski, E. Brzychczy, and G. J. Nalepa. “KnAC: an approach for enhancing cluster analysis with background knowledge and explanations.” CoRR, abs/2112.08759, 2021 https://doi.org/10.48550/arXiv.2112.08759
   * [16] S. Bobek, M. Kuk, G. J. Nalepa, "Enhancing cluster analysis with explainable AI and multidimensional cluster prototypes," in IEEE Access, (submitted, under review)
   * [17] S. Bobek, M. Kuk, J. Brzegowski, E. Brzychczy, and G. J. Nalepa. “KnAC: an approach for enhancing cluster analysis with background knowledge and explanations.” Applied Intelligence (submitted, under second round review)