preprints

  1. P. Hartono, Mixing autoencoder with classifier: conceptual data visualization, arXiv:1912.01137 [cs.LG]
  2. P. Hartono, Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder arXiv:2004.01481 [physics.soc-ph]

Under review for journals

  1. R. Matsunaga, P. Hartono, J. Abe, Bootstrapping learning of musical scales: Culture-general implications from DRNN simulations
  2. G. E. Seme, I. Mukhlash, P. Hartono, Unveiling the Drivers of Volatility: A SHAP-Based Interpretation of LQ45 Prediction using a Hybrid LSTM-GRU Model
  3. M. Oriyama, P. Hartono, H. Sawada, Human-in-the-loop Transfer Learning in Collision Avoidance of Autonomous Robots
  4. 市川 槙人、藤田 実沙、ハルトノ ピトヨ、生成AIを用いる遺伝的アルゴリズムの初期解生成

Journals and Letters

  1. P. Hartono, Context-Flexible Cartography with Siamese Topological Neural Networks, Discover Artificial Intelligence (2024). DOI:10.1007/s44163-023-00098-w
  2. S. Sendari, Muladi, F. Ardiyansyah, S. Setumin, H. Lin, P. Hartono, Common-sensical Incentive Reward in Deep Actor-Critic Reinforcement Learning for Mobile Robot Navigation, Journal of Innovative Computing, Information and Control (IJICIC) Vol. 20, No. 2, pp. 373-389 (2024). DOI:10.24507/ijicic.20.02.373
  3. F. Masyfa, T. Hambali, H. Tolle, P. Hartono, Nonlinear Level Adjustment based on Fibonacci Sequence for Optimizing Player’s Performance on Maze Game, International Journal of Serious Games, Vol. 10, No. 2, pp. 137-157 (2023). DOI:10.17083/ijsg.v10i2.586
  4. S. Kobayashi, P. Hartono, Eye-Contact as a new modality for man-machine interface, International Journal of Advanced Computer Science and Applications, Vol. 14, No. 3, pp. 42-49 (2023). DOI:1014569/IJACSA.2023.0140306
  5. H. Kishi, P. Hartono, Pain-Illusion for Human-Machine Interface (in Japanese: 痛覚錯覚を用いたインタフェースの開発), Journal of Signal Processing, Vol. 27, No. 1, pp. 17-27 (2023).DOI:10.2299/jsp.27.17 ( best paper award )
  6. G.E. Setyawan, P. Hartono and H. Sawada , Cooperative Multi-Robot Hierarchical Reinforcement Learning, International Journal of Advanced Computer Science and Applications (IF: 1.16). Vol. 13, No 9 (2022). DOI: 10.14569/IJACSA.2022.0130904
  7. K. Ning, P. Hartono, H. Sawada, Using inverse learning for controlling bionic robotic fish with SMA actuators, MRS Advances, Vol. 7, pp. 649–655 (2022) DOI: 10.1557/s43580-022-00328-w
  8. K. Ogawa, P. Hartono, Infusing common-sensical prior knowledge into topological representations of learning robots, Artificial Life and Robotics 27 pp. 576-585 (2022). (IF: 1.037) DOI: 10.1007/s10015-022-00776-5
  9. G.E. Setyawan, H. Sawada and P. Hartono, Combinations of Micro-Macro States and Subgoals Discovery In Hierarchical Reinforcement Learning For Path Finding, International Journal of Innovative Computing, Information and Control (IJICIC)(IF: 1.16), Vol. 18, No. 2, pp. 447-461 (2022). DOI:10.24507/ijicic.18.02.447
  10. K. Ogawa, P. Hartono, Collaborative General Purpose Convolutional Neural Network, Journal of Signal Processing, Vol. 25, No. 2, pp.53-61 (2021) DOI: 10.2299/jsp.25.53 ( best paper award )
  11. P. Sabol, P. Sincak, P. Hartono, P. Kocan, Z. Benetinova, A. Blicharova, E. Stammova, A. Fabianova-Sabolova and A. Jackova, Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images, Journal of Biomedical Informatics (IF: 3.526), Vol. 109, 103523 (2020) DOI:10.1016/j.jbi.2020.103523
  12. P. Hartono, Similarity Maps and Pairwise Prediction for Transmission Dynamics of COVID-19 with Neural Networks, Informatics in Medicine Unlocked (IF: 2.110) Vol. 20, 100386 (2020) DOI:10.1016/j.imu.2020.100386
  13. P. Hartono, Mixing autoencoder with classifier: conceptual data visualization, IEEE Access (IF: 4.098), Vol. 8, pp.105301 -105310 (2020) DOI: 10.1109/ACCESS.2020.2999155
  14. P. Hartono, A Transparent Cancer Classifier, Health Informatics Journal (2020) (IF: 2.297) Vol.26, No.1, pp. 190-204 (2020) DOI:10.1177/1460458218817800.
  15. Matsunaga, R., Hartono, P., Yokosawa, K., & Abe, J., The development of sensitivity to tonality structure of music: Evidence from Japanese children raised in a simultaneous and unbalanced bi-musical environment, Music Perception Vol. 37, No. 3, pp. 223 - 237 (2020). DOI: 10.1525/mp.2020.37.3.225 (IF: 1.152)
  16. P. Sabol, P. Sincak, J. Magyar, P. Hartono, Semantically Explainable Fuzzy Classifier, International Journal of Pattern Recognition and Artificial Intelligence (IF: 1.11), Vol.34, No.4 (2020) DOI:10.1142/S0218001420510064.
  17. P. Hartono, T. Trappenberg, Topographical representation adds robustness to supervised learning, Journal of Intelligent & Fuzzy Systems, Vol. 36, pp. 3249-3262 (2019) (IF: 1.637) DOI:10.3233/JIFS-18343
  18. R. Matsunaga, T. Yasuda, M. Johnson-Motoyama, P. Hartono, K. Yokosawa, J. Abe, A Cross-cultural Comparison of Tonality Perception in Japanese, Chinese, Vietnamese, Indonesian, and American Listeners, Psychomusicology: Music, Mind and Brain, Vol. 28, No. 3, pp. 178-188 (2018). DOI: 10.1037/pmu0000219
  19. P. Hartono, Classification and Dimensional Reduction using Restricted Radial Basis Function Networks, Neural Computing and Applications, Vol. 30(3), pp. 905-915 (2018). DOI:10.1007/s00521-016-2726-5 (IF: 4.664)
  20. P. Hartono, K. Ogawa, Intuitive Analysis by Visualizing Context Relevant E-learning Data, IPSJ Trans. on Computers and Education, Vol. 3. No. 2, pp. 20-27 (2017). pdf
  21. P. Hartono, P. Hollensen, T. Trappenberg, Learning-Regulated Context Relevant Topographical Map, IEEE Trans. on Neural Networks and Learning Systems, Vol. 26, No. 10, pp. 2323-2335 (2015). DOI:10.1109/TNNLS.2014.2379275 (IF: 11.683)
  22. R. Matsunaga, P. Hartono, J. Abe, The acquisition process of musical tonal schema: Implications from connectionist modeling, Frontiers in Psychology (IF: 2.129) 6:1348 (2015). DOI:10.3389/fpsyg.2015.01348
  23. A. Ishikawa, K. Ogawa, P. Hartono, Discovering Students Characteristics Using Learning History Data, The Journal of Information and Systems in Education, Vol.32 No.2 pp.185-196 (2014). (in Japanese) pdf
  24. P. Hartono, and R. Yoshitake, Automatic Playlist Generation from Self-Organizing Music Map, Journal of Signal Processing, Vol. 17, No.1, pp. 11-17 (2013). DOI:10.2299/jsp.17.11
  25. P. Hartono, and A. Nakane, Robotics Modules with Real-time Adaptive Topology, International Journal of Computer Information Systems and Industrial Management, Vol. 3, pp. 185-192 (2011).pdf
  26. P. Hartono, and A. Nakane, Adaptive Coupled Oscillators for Modular Robots, IEEJ Trans. on Electronics, Information and Systems, Vol. 131, No. 3, pp. 602-603 (2011).
  27. P. Hartono, and S. Kakita, Fast reinforcement learning for simple physical robots, Memetic Computing (IF: 2.674) , Vol. 1, No.4, pp. 305-313 (2009). DOI:10.1007/s12293-009-0015-x
  28. P. Hartono, Ensemble of Linear Experts as an Interpretable Piecewise Linear Classifier, Innovative Computing, Information and Control Express Letters Vol. 2, No. 3, pp. 295-303 (2008).pdf
  29. P. Hartono, and S. Hashimoto, Learning from Imperfect Data, Applied Soft Computing Journal (IF: 4.873) Vol.7, No. 1, pp. 353-363 (2007). DOI:10.1016/j.asoc.2005.07.005
  30. P. Hartono, and S. Hashimoto, Nonlinear Classification using Ensemble of Linear Perceptrons, Neural Information Processing-Letters and Reviews, Vol. 10, No.2, pp.35-41 (2006).
  31. N. Kobori, K. Suzuki, P. Hartono, S. Hartono, Reinforcement Learning with temperature distribution based on likelihood function, Journal of the Japanese Society for Artificial Intelligence, Vol. 20, No. 4D pp. 297-305 (2005). (in Japanese). DOI:10.1527/tjsai.20.297
  32. A. Sudou, P. Hartono, R. Saegusa, S. Hashimoto, Signal reconstruction from sampled data tainted by aliasing phenomena using neural network, Journal of Signal Processing Vol.7, No.1, pp. 5-13 (2003).
  33. P. Hartono and S. Hashimoto, Extracting the Principal Behavior of a Probabilistic Supervisor through Neural Network Ensemble, International Journal of Neural Systems (IF: 6.4), Vol. 12, Nos. 3 & 4, pp. 291-301 (2002). DOI:10.1142/S0129065702001126
  34. H. Qi, P. Hartono, K. Suzuki, S. Hashimoto, Sound database retrieved by sound, Acoustical Science and Technology, Vol. 23, No. 6, pp. 293-300 (2002).DOI:10.1250/ast.23.293
  35. P. Hartono and S. Hashimoto, Learning From Imperfect Supervisor Using Neural Network Ensemble, Journal of Information Processing Society of Japan, Vol. 42, No. 5, pp.1214 ?1222 (2001).pdf
  36. P. Hartono, S. Hashimoto, Temperature Switching in Neural Network Ensemble, Journal of Signal Processing, Vol. 4, No. 5, pp. 395-402 (2000).

International Conferences

  1. I. Suzuki, P. Hartono, Human-in-the-loop: infusing knowledge into neural networks, Proc. 2024 IEEE Int. Conf. on Mechatronics and Automation, pp. 1665-1670 (2024). doi: 10.1109/ICMA61710.2024.10633061.
  2. P. Hartono, Topological Neural Networks: theory and applications, 2023 World Symposium on Digital Intelligence for Systems and Machines (DISA), Košice, Slovakia, 2023, pp. 84-89, doi: 10.1109/DISA59116.2023.10308945.
  3. Oriyama, M., Hartono, P., Sawada, H. (2024). Human-Guided Transfer Learning for Autonomous Robot. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1961. Springer, Singapore. doi: 10.1007/978-981-99-8126-7_15
  4. G.E. Setyawan, P. Hartono, H. Sawada, An In-Depth Analysis of Cooperative Multi-Robot Hierarchical Reinforcement Learning, The 7th International Conference on Sustainable Information Engineering and Technology, pp. 117-126 (2022) (Best Paper Award) DOI: 10.1145/3568231.3568258.
  5. K. Ogawa, P. Hartono, Infusing prior knowledge into topological representations of learning robots, Proc. 27th Int. Symposium on Artificial Life and Robotics, pp. 347-352 (2022) (Young Author Award).
  6. S. Kuramoto, H. Sawada, P. Hartono, Visualization of topographical internal representation of learning robots, Proc. Int. Joint. Conf. on Neural Networks (IJCNN 2020) DOI: 10.1109/IJCNN48605.2020.9206675.
  7. S. Torabi, M. Wahde, P. Hartono, Road grade and vehicle mass estimation for heavy-duty vehicles using feedforward neural networks, Proc. The 4th International Conference on Intelligent Transportation Engineering 2019 pp. 316 - 321. DOI: 10.1109/ICITE.2019.8880261
  8. P. Sabol, P. Sincak, K. Ogawa, P. Hartono, Explainable Classifier Supporting Decision-making for Breast Cancer Diagnosis from Histopathological Images, Proc. Int. Conf. on Neural Networks (IJCNN 2019) DOI: 10.1109/IJCNN.2019.8852070.
  9. Y. Kinouchi, K. J. Mackin, P. Hartono, A conscious AI based on recurrent neural network applying dynamic information equilibrium, AAAI Spring Symposium on Towards Conscious AI Systems 2019
  10. P. Hartono, Topographical Internal Representation in Deep Neural Networks, Proceedings 1st World Symposium on Digital Intelligence for Systems and Machines (DISA 2018), pp. 77-82 (2018). DOI: 10.1109/DISA.2018.8490600
  11. Patrik Sabol, Peter Sincak, Jan Busa, and Pitoyo Hartono, Cumulative Fuzzy Class Membership Criterion Decision Based Classifier, Proc. IEEE SMC 2017 pp. 334-339,Banff, Canada (2017).
  12. P. Hartono and Y. Take, Pairwise Elastic Self-Organizing Maps, Proc. 12th Int. Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM+ 2017), pp. 230-236 (2017).DOI:10.1109/WSOM.2017.8020006
  13. Jaroslav Ondo, Peter Sinčák, Pitoyo Hartono, Improvement of MF-ARTMAP Classification Accuracy Using Simulated Annealing, Proc. IEEE SMC 2016, pp. 2038-2043 (2016). DOI: 10.1109/SMC.2016.7844540
  14. T. Trappenberg, P. Hollensen,P. Hartono, Classifier with Hierarchical Topographical Maps as Internal Representation, International Conference on Learning Representations (ICLR 2015, workshop track) http://arxiv.org/abs/1412.6567
  15. P. Hartono and K. Ogawa, Visualizing Learning Management System Data using Context-Relevant Self-Organizing Map, Proc. IEEE SMC 2014, pp. 3502-3506 (2014).
  16. P. Hartono, P. Hollensen, T. Trappenberg, Visualizing Hierarchical Representation in a Multilayered Restricted RBF Network, Proc. International Conference on Artificial Neural Networks (ICANN 2014), LCNS 8681, pp. 339-346 (2014).
  17. Matsunaga, R., Hartono, P., & Abe, J. (2013). Tonal schema learning in an artificial neural network. Proceedings of the Society for Music Perception and Cognition 2013, 1, pp. 131.
  18. P. Hartono, T. Trappenberg, Classificability-regulated Self-Organizing Map using Restricted RBF, Proc. IEEE IJCNN 2013, pp. 160-164 (2013).
  19. P. Hartono, Computational Intelligence for Creating Autonomous Robots, Robot Intelligence Technology and Applications 2012, Advances in Intelligent Systems and Computing Volume 208, Springer, pp. 733-740 (2013).
  20. P. Hartono and T. Trappenberg, Internal Representation of Sensory Information for Training Autonomous Robot, Proc. SCIS-ISIS 2012, pp. 341-345 (2012).
  21. P. Hartono and T. Trappenberg, Internal Topographical Structure in Training Autonomous Robot, IEEE SMC 2011, pp. 239-243 (2011).
  22. P. Hartono, Ensemble of Perceptrons with Confidence Measure for Piecewise Linear Decomposition, Proc. IEEE Int. Joint Conf. on Neural Networks (IJCNN 2011), pp. 648-653 (2011). (IJCNN 2011).
  23. P. Hartono and T. Trappenberg, Autonomous Robot with Internal Topological Representation, The 3rd Int. Conf. on Cognitive Neurodynamics.
  24. P. Hartono, Utilization of Machine Learning Methods for Assembling, Training and Understanding Autonomous Robots, Int. Conf. on Human System Interaction (HSI 2011), pp. 398-402 (2011).
  25. P. Hartono and A. Nakane, Modular Robot with Adaptive Connection Topology, Proc. 10th Int. Conf. on Hybrid Intelligent Systems (HIS 10), pp. 191-196 (2010).
  26. T. Trappenberg, A. Saito, P. Hartono, Selective attention improves self-organization of cortical maps with multiple inputs, IEEE Int. Join Conf. on Neural Networks (IJCNN 2010), pp. 1-4 (2010).
  27. P. Hartono, and T. Trappenberg, Learning Initialized by Topologically Correct Map, Proc. IEEE Int. Conf. on Systems, Man and Cybernetics (SMC) 2009, pp. 2802-2806 (2009).
  28. T. Trappenberg, P. Hartono, and D. Rasmusson, Top-down control of learning in biological self-organizing maps, 7th. International Workshop on Self-Organizing Maps (WSOM 2009), LNCS 5629 pp. 316-324 (2009).
  29. P. Hartono, and A. Saito, Class-Proximity SOM and its Applications in Classification, Proc. IEEE Int. Conf. on Systems, Man and Cybernetics (SMC) 2008, pp. 2150-2155 (2008).
  30. P. Hartono, Interpretable Piecewise Linear Classifier, Proc. ICONIP 2007, LNCS 4985, pp. 434-443 (2007).
  31. Petterson, J., Hartono, P., and Wahde, M., A behavior module for odometry recalibration in autonomous robots, Proc. of the 4th Int. Symp. on Autonomous Minirobots for Research and Edutainment (AMiRE2007), pp. 11-18 (2007).
  32. P. Hartono, S. Hashimoto, Ensemble as a Piecewise Linear Classifier, Proc. Int. Conf. on Hybrid Intelligent Systems, CD Proceedings (2006).
  33. P. Hartono, S. Hashimoto, Analysis on the Performance of Ensemble of Perceptrons, Proc. IJCNN 2006, pp. 10627-10632 (2006).
  34. P. Hartono, M. Speiser, Acquirement of Simple Moving Sequences in Mobile Robot through Utility Function-based Behavior Selection, Proc. SCIS & ISIS 2006, pp. 967- 970 (2006).
  35. P. Hartono, S. Hashimoto, Learning with Ensemble of Linear Perceptron, Proc. ICANN 2005, LNCS 3697, Springer-Verlag, pp. 115-120 (2005).
  36. P. Hartono and S. Hashimoto, A Robust Face Detector Algorithm Utilizing Neural Network and Partial Template Matching, Proc. of SPIE Machine Vision and its Optomechatronic Application, pp. 119-127 (2004).
  37. P. Hartono, S. Hashimoto, "Ensemble of Linear Perceptrons with Confidence Level Output," his, pp.186-191, Fourth International Conference on Hybrid Intelligent Systems (HIS'04), (2004).
  38. P. Hartono and S. Hashimoto, and M. Wahde, Labeled-GA with Adaptive Mutation Rate, Proc. IEEE CEC 2004, pp. 1851-1858 (2004).
  39. M. Nakakita, P. Hartono, S. Hashimoto, Face Detection with A Neural Network Posture Predictor and Partial Template Matching, Proc. IASTED International Conference on Vizualization, Imaging and Image Processing, pp. 575-580 (2003).
  40. P. Hartono, K. Tabe, K. Suzuki, S. Hashimoto, Strategy Acquirement by Survival Robots in Outdoor Environment, Proc. 2003 IEEE International Conference on Robotics and Automation, pp. 3571-3575 (2003).
  41. P. Hartono and S. Hashimoto, Adaptive neural network that learns from imperfect supervisor, Proc. ICONIP’02, pp. 2561-2565 (2002).
  42. N. Kobori, K. Suzuki, P. Hartono and S. Hashimoto, Learning to control a joint driven double inverted pendulum using nested actor/critic algorithm, Proc. ICONIP’02, pp. 2610-2614 (2002).
  43. P. Hartono, K. Suzuki, H. Qi, S. Hashimoto, Subjective Preference Oriented Global Sound Database, Proc. International Computer Music Conference 2002 pp. 446-449 (2002).
  44. A. Sudou, P. Hartono, R. Saegusa, S. Hashimoto, Signal Reconstruction From Sampled Data Using Neural Network, Proc. IEEE Workshop on Neural Networks for Signal Processing 2002, pp. 707-715 (2002).
  45. K. Tabe, K. Suzuki, P. Hartono, S. Hashimoto, Survival Strategy Learning for Autonomous Mobile Robot, Proc. IEEE-RAS Int. Conf. On Humanoid Robots 2001, pp. 485-492 (2001).
  46. P. Hartono and S. Hashimoto, Migrational GA that preserves Solutions in Non-Static Optimization Problems, Proc. of SMC 2001, pp. 255-260 (2001).
  47. K. Suzuki, H. Yamada, P. Hartono and S. Hashimoto: Modeling of Interrelationship between Physical Feature of Face and Its Impression, Proc. of SHF2001, pp. 310-315 (2001).
  48. P. Hartono and S. Hashimoto, Learning-data Selection Mechanism through Neural Network Ensemble, In Kittler, J. and Roli, F. (Eds), Multiple Classifier Systems, pp. 188-197, Springer (2001).
  49. R. Saegusa, P. Hartono, and S. Hashimoto, Position-based Competition Learning of Neural Networks Array, Proc. Int. Joint Conf. On Neural Networks 2001 (IJCNN2001), pp. 2817-2820 (2001).
  50. S. Nakamura, M. Sawada, Y. Aoki, P. Hartono and S. Hashimoto, Flower image database construction and its retrieval, Proc. of Korea-Japan Joint Workshop on Computer Vision, pp. 37-42 (2001).
  51. P. Hartono and S. Hashimoto, Effective Learning in Noisy Environment using Neural Network Ensemble, Proc. International Joint Conference on Neural Networks 2000 (IJCNN 2000), II-179-185 (2000).
  52. P. Hartono and S. Hashimoto, Neural Network Ensemble and Its Application in Probabilistic Learning、Proc. Fourth Int. Conference on Cognitive and Neural System, pp. 18 (2000).
  53. T. Notsu, P. Hartono, S. Hashimoto and H. Sawada: Multi-modal Gesture Database and Gesture Recognition Using Wearable Devices, Proc. 6th Korea-Japan Workshop on Computer Vision, pp.163-168 (2000).
  54. P. Hartono and S. Hashimoto: Ensemble of Neural Network With Temperature Control, Proc. 1999 Int. Join Conference on Neural Networks 1999 (IJCNN 1999), pp.4073-4078 (1999).
  55. P. Hartono, K. Asano, W. Inoue and S. Hashimoto: Adaptive Timbre Control Using Gesture, Proc. International Computer Music Conference 1994, pp.151-158 (1994).
  56. P. Hartono and S. Hashimoto: Active Learning Algorithm of Neural Network, Proc. 1993 International Joint Conference on Neural Network 1993 (IJCNN 1993), pp.2548-2551 (1993).