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Bernhard Moser is President of Austrian Society for Artificial Intelligence (asai)

Short biography

Priv.-Doz. Mag. Dr. Bernhard A. Moser studied mathematics in Salzburg and Linz, where he habilitated at the Johannes-Kepler-University Linz (JKU) with a fundamental topic in the field of artificial intelligence in the Department of Mathematics. His professional career is closely linked to the Software Competence Center Hagenberg (SCCH), where he held various research development and management roles for over 20 years, such as Area Manager for Data Analysis and Research Director for the strategic orientation of the center. SCCH is an Austrian COMET center for applied software, data science and AI with currently around 130 employees.

Bernhard Moser was always keen to bring business and science together. This has enabled him to realize over 50 million euros in cooperative research projects at SCCH. He is currently coordinator of the European H2020 project TEAMING.AI on human-AI cooperation and the COMET module S3AI, a basic research project on the adaptation of machine learning models and their safety and security. Since January 2023, he has been focusing on the mathematical foundations of spiking neural networks at the Institute for Signal Processing at JKU.

In his honorary role as President of the Austrian Society for Artificial Intelligence (ASAI), which he has held since 2020, as well as in his new role as a member of the AI Advisory Board of the Austrian Federal Government and the newly installed AI Service Centre, he is committed to strengthening Austria as a research location in the field of AI.

Research Interests

• AI research comprising a) mathematical foundation of machine/deep learning,b) AI system engineering, and c) applications in computer vision,  manufacturing, life science and mobility; foundations of neuromorphic computing

Personal Interests

• Philosophy and history

Profile in a Nutshell

• Since 28th February 2020: : President of Austrian Society of Artificial Intelligence
• Research Management: Mastermind of COMET proposal for funding period 2 (01/2019- 12/2022) with a budget of about 17 Mio Euro (positive evaluation March 2018; without any requirements); 
• Project Acquisition, e.g., mastermind and coordinator of COMET Module S3AI with 8 PhD (4 years, start 01/2020); acquisition of international projects: FP7-ICT ADVANCE, EraSME EU-Projekt TexQuality, Marie Skłodowska-Curie ITN InCeM, H2020 ALOHA, H2020 TRESSPASS; recently, submission of H2020 ICT-38 proposal TEAMING.AI on Human-AI Teaming at 16th of Jan, 2020, as coordinator;
• Publications in top ranked journals, e.g., Journal for Machine Learning Research (JLMR), IEEE Trans. on Pattern Analysis and Machine Intelligence (IEEE-TPAMI), IEEE Trans. on Signal Processing (IEEE-TSP), IEEE Trans. on Fuzzy Systems (IEEE-TFS), Journal for Discrete & Computational Geometry (DCG); 
• Conferences: Co-organizer of conferences and workshops, e.g., EBCCSP 2018 (Co-General Chair with M. Miśkowicz), DEXA 2019 (Workshop on Machine Learning meets Knowledge Graph), ACM WiSec 2020 (Workshop WiseML  with Y. Shi und Y. Sagduyu);
• Master and PhD Theses: currently co-supervision of 5 PhDs;
• Scientific Community Building: nominated as next president of the Austrian Society for Artificial Intelligence (General Assembly 28th of February 2020);

Study and Employment

Project Acquisition

• TEAMING.AI (RIA H2020 ICT-38, SEP-210640996, AI in Manufacturing, submission at 16th of Jan.2020; Coord. SCCH/B.Moser) on Human-AI Teaming Platform for Maintaining and Evolving AI Systems in Manufacturing;
• S3AI (Austrian COMET Module, Jan 2020 – Dec 2023, Coord. SCCH/B.Moser): Safe and Secure Shared AI by Deep Model Design; main partners: JKU/ML Institute (S.Hochreiter); JKU/RISC (J. Schicho), ÖAW/RICAM (S. Pereverzyev); KU Leuven (B.Preneel);
• AI@Work (Austrian FFG, IKT der Zukunft, Exploratory Project for AI Flagship, Oct 2019- Sept. 2020; Coord. SCCH/B.Moser) on Human Centered AI in Digitized Working Environments; main partners: Profactor, JKU/Inst. für Arbeitsforschung und Arbeitspolitik; 
• AutoQualI (Austrian FFG, Produktion der Zukunft, June 2018 – Dec 2020, Coord. MIBA): Transfer Learning for Industrial Quality Inspection; main partner: MIBA, RECENDT;
• TRESSPASS (IA H2020-SEC-2016-2017-2, June 2018 – Nov 2021; Coord. Demokritos): robusT Risk basEd Screening and alert System for PASSengers and luggage; main partner: Fraunhofer;
• ALOHA (RIA H2020, ICT-05-2017, Customized and low energy computing, Jan 2018 – Dec 2020, Coord. Uni Cagliari/P. Meloni): on a software framework for runtime-adaptive and secure deep learning on embedded systems; main partner: ETH Zurich, IBM Israel, PKE Holding;
• VISIOMICS (Austrian FFG, COIN, Nov 2017 – Oct 2019, Coord. Labdia Labordiagnostik GmbH): Analysis platform for better relapse prediction in child cancer; main partner: Childen’s Cancer Research Institute (St. Anna Krebsforschung);
• InCeM (Marie Skłodowska-Curie Innovative Training Networks (ITN-ETN), H2020-MSCA-ITN-2014, Jan 2015 – Dec2018, Coord. Universtitätsklinikum Aachen): Research Training Network on Integrated Component Cycling in Epithelial Cell Motility; main partners: Forschungszentrum Julich, University of Sussex;
• Hyperion3D (Austrian FFG, IKT der Zukunft, June 2013-Dec  2016, Coord. SCCH/B.Moser): Intelligent Workflow Design for Low-Cost 3D Film Production; main partners: TU Vienna/ Institute of Visual Computing and HumanCentered Technology (M. Gelautz); Emotio3D (F. Seitner);
• WiMOis (Austrian FFG, Bridge, July 2014 – June 2017, Coord. SCCH/B. Moser): Knowledge-based Modeling of Individual Game Performance of Athletes in Team Sports; main partner: Uni Wien/Institut für Sportwissenschaft (R. Leser), abatec-ag;
• SKIN (Austrian FFG, KIRAS/Security, July 2014 – June 2017, Coord. PKE Electronics AG/W. Kloihofer): Protection of the outer shell of critical infrastructure buildings; main partner: Austrian Federal Ministry of the Interior (BMI);
• ADVANCE (FP7-ICT, ICT-2009.3.6 - Computing Systems ‚  Febr. 2010 – Sept. 2013, Coord. Uni Hertfordshire/A. Shafarenko): Asynchronous and Dynamic Virtualization through performance ANalysis for support Concurrency Engineering; main partner: Heriot Watt University;
• TexQuality (EraSME EU-Project, July 2010 - June 2012, Coord. SCCH/B. Moser): High-Performance Quality Inspection of Industrial Textile Fabrics by 3D Profile Analysis; main partner: Hochschule Rosenheim (Prof. Dr. H.  Ernst);
• Similarity (Austrian FWF P21496, June 2010- June 2013, Coord SCCH/B. Moser): fundamental research project on Hermann Weyl’s discrepancy measure as image similarity;

Co-Supervision of Master and PhD Theses (last year and upcoming)

• Dmytro Kotsur (affiliation SCCH; PhD; co-supervision with Prof. S. Pereverzyev (RICAM, Austrian Academy of Sciences) and J. Mattes(MattesMedical), defense at Uni Kiew, Oct. 2019) on modeling cell-motility by regularized elastic active contour models;
• Lukas Gruber (affiliation JKU/ML Inst.; Master; members of the examination commission; co-supervision with J. Kofler and Prof. S. Hochreiter (JKU/ML Inst.); examination Jan. 2020) on integral probabilistic metrics for generative adversarial networks (GANs);
• Werner Zellinger (affiliation SCCH; PhD; co-supervision with Prof. S. Saminger-Platz (JKU, Mathematics), expected defense April 2020) on domain adaptation for neural networks via moment alignment;
• Christoph Heindl (affiliation PROFACTOR, PhD;  co-supervision with Prof. J. Scharinger (JKU, CP Institute), expected defense 2021) on scalable human in the loop (HITL) for collaborative robotics by model-guided synthesis of data;
• Natalia Shepeleva (affiliation SCCH; PhD ; co-supervision with Prof. S. Hochreiter (JKU, ML Institute), start 2020)  on exploiting polyhedral tessellation space for regularizing deep neural networks;
• Michal Lewandowski (affiliation SCCH; PhD ; co-supervision with Prof. B. Biggio (Uni Cagliari/Italy), start 2020) on exploiting polyhedral tessellation space for regularizing deep neural networks;
• Simon Hasler (affiliation RubbleMaster; PhD; co-supervision with Prof. J. Scharinger (JKU, CP Institute), start 2019) on knowledge-graph based human-AI teaming framework for semantically enhanced human in the loop (HITL);

Journal Publications

• H. Eghbal-Zadeh, L. Fischer, N. Popitsch, F. Kromp, S. Taschner-Mandl, K. Koutini, T. Gerber, E. Bozsaky, P. F. Ambros, I. M. Ambros, G. Widmer, B. A. Moser, Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic Data, Journal of Computational Biology, Vol 26, Nr 6, pages 572-596, June 2019.
• B. Moser, M. Lunglmayr. On quasi-isometry of threshold-based sampling. IEEE Transactions on Signal Processing, volume 67, number 14, pages 3832-3841, DOI: 10.1109/TSP.2019.2919415, May, 2019.
• W. Zellinger, B. A. Moser, T. Grubinger, E. Lughofer, T. Natschläger, S. Saminger-Platz, Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment, Information Sciences, Vol. 483, pp. 174-191, May 2019.
• B. A. Moser, Similarity Recovery from Threshold-Based Sampling under General Conditions, IEEE Transactions on Signal Processing, Vol. 65, Issue 17, 4645 – 4654, 2017.
• W. Reisner, B. Moser, Improving Visual Discomfort Prediction for Stereoscopic Images via Disparity-Based Contrast, Journal of Imaging Science and Technology, 11/2015; 59(6):60401-1-60401-8. DOI: 10.2352/J.ImagingSci.Technol.2015.59.6.060401.
• R. Leser, B. Moser, T. Hoch, J. Stöger, G. Kellermayr, S. Reinsch, A. Baca. Expert-oriented modelling of a 1vs1situation in football. International Journal of Performance Analysis in Sport, volume 15, number 3, pages 949-966, December, 2015.
• B. Moser. The Range of a Simple Random Walk on Z: An Elementary Combinatorial Approach, Electronic Journal of Combinatorics, 21(4):#P4.10, 2014.
• B. Moser. On a Multisection Style Binomial Summation Identity for Fibonacci Numbers, Int. J. Contemp. Math. Sciences, 9(4):175--186, 2014.
• B. Moser and T. Natschläger. On Stability of Distance Measures for Event Sequences Induced by Level-Crossing Sampling, IEEE Trans. on Signal Processing, 62(8):1987--1999, 2014.
• B. Moser: Geometric characterization of Weyl’s Discrepancy Norm in terms of its n-dimensional unit balls, Journal Discrete & Computational Geometry (DCG), volume 48, number 4, pages 793-806, 2012.  H. Schöner, F. Bauer, A. Dorrington, B. Heise, V. Wieser, A. Payne, M. Cree, B. Moser. Image processing for 3D-scans generated by time of flight range cameras, Journal of Electronic Imaging, vol. 21, nr  2, DOI:10.1117/1.JEI.21.2.023012, 2012.
• V. Wieser, C. Grelck, J. Guo, P. Haslinger, F. Korzeniowski, B. Moser, S. Scholz. Combining high productivity and high performance in image processing using single assignment C on multi-core CPUs and many-core GPUs, Journal of Electronic Imaging, volume 21, number 2, DOI: 10.1117/1.JEI.21.2.021116, 2012.  B. Moser. A similarity measure for image and volumetric data based on Hermann Weyl's discrepancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 33, nr  11, pages 2321-2329, 2011.
• B. Moser: On the compactness of admissible transformations of fuzzy partitions in terms of T-equivalence relations. Fuzzy Sets and Systems, vol. 160 (1), pp. 95-106, 2009.
• B. Moser: On the T-Transitivity of Kernels, Fuzzy Sets and Systems, vol 157, pp. 1787-1796, 2006.
• B. Moser: On Representing and Generating Kernels by Fuzzy Equivalence Relations,  Journal of Machine Learning Research, MIT Press Cambridge, MA, USA, vol 7, 2603-2620, 2006.
• B. Moser and M. Navara. Fuzzy Controllers with Conditionally Firing Rules. IEEE Trans. Fuzzy Systems, 10(3):340– 348, 2002.   D. Tikk, I. Joó, L. T. Kóczy, P. Várlaki, B. Moser, and T. D. Gedeon. Stability of interpolative fuzzy KH-controllers, Fuzzy Sets and Systems, 125:105–119, 2002. 
• B. Moser and R. Winkler. A relationship between equality relations and the T-redundancy of fuzzy partitions and its application to Sugeno controllers, Fuzzy Sets and Systems, 114:455–467, 2000. 
• E. P. Klement, L. T. Kóczy and B. Moser, Are Fuzzy Systems Universal Approximators? Int. J. General Systems, 28(2– 3):259–282, 1999. 
• B. Moser. Sugeno controllers with a bounded number of rules are nowhere dense. Fuzzy Sets and Systems, 104(2):269–277, 1999. 
• B. Moser, E. Tsiporkova and E. P. Klement. Convex combinations in terms of triangular norms: A characterization of idempotent, bisymmetrical and self-dual compensatory operators. Fuzzy Sets and Systems, 104:97–108, 1999. 
• E. P. Klement and B. Moser. On the T-redundancy of fuzzy partitions. Fuzzy Sets and Systems, 85:195–201, 1997.

Papers in Proceedings of Conferences with Peer Review

• M. Kumar, M. Roßbory, B. Moser, B. Freudenthaler. Deriving an optimal noise adding mechanism for privacypreserving machine learning. In G. Anderst-Kotsis, A. Tjoa, I. Khalil, E. al. (editors), Database and Expert Systems Applications - Proc DEXA 209 International Workshops, Communications in Computer and Information Science, volume 1062, pages 108-118, DOI 10.1007/978-3-030-27684-3_15, Springer, August, 2019.
• M. Lunglmayr, B. Moser, S. Qaisar. Tradeoff analysis of discrepancy-based adaptive thresholding approach. Proceedings of the 5th International Conference on Event-based Control, Communication, & Signal Processing (EBCCSP 2019), pages 1-4, DOI 10.1109/EBCCSP.2019.8836905, IEEE, September, 2019.
• S. Qaisar, S. Laskar, M. Lunglmayr, B. Moser, R. Abdulbaqi, R. Banafia. An event-driven approach for time-domain recognition of spoken english letters. Proceedings of the 5th International Conference on Event-based Control, Communication, & Signal Processing (EBCCSP 2019), 1-4, DOI 10.1109/EBCCSP.2019.8836903, September, 2019.  P. Meloni, D. Loi, G. Deriu, A. Pimentel, D. Saprat, M. Pintort, M. Pintort, B. Biggio, O. Ripolles, D. Solans, F. Conti, L. Benini, T. Stefanov, S. Minakova, B. Moser, N. Shepeleva, M. Masin, F. Palumbo, N. Fragoulis, I. Theodorakopoulos. Architecture-aware design and implementation of CNN algorithms for embedded inference: The ALOHA project. Proceedings of 2018 30th International Conference on Microelectronics (ICM 2018), pages 52-55, DOI 10.1109/ICM.2018.8704093, IEEE, May, 2019.
• H. Eghbal-zadeh, L. Fischer, N. Popitsch, F. Kromp, S. Taschner-Mandl, K. Koutini, T. Gerber, E. Bozsaky, P. F. Ambros, I. M. Ambros, G. Widmer, B. A. Moser, Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic Data, arxiv.org/abs/1806.08840, accepted at the Joint ICML and IJCAI 2018 Workshop on Computational Biology, July 2018.
• N. Shepeleva, T.Hoch, L. Fischer, W. Kloihofer, B.Moser, Removing nuisance in tracklet data, Deep Learning and Video Content Analysis, 10802-28, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies, SPIE Security and Defense, 10-11 Sept. 2018.
• B. A. Moser, On the Discrepancy Normed Space of Event Sequences for Threshold-based Sampling, EBCCSP 2018, arXiv:1806.06273v1.
• W. Zellinger, B. Moser, A. Chouikhi, M. Gelautz, F. Seitner and M. Nezveda. Linear optimization approach for depth range adaption of stereoscopic videos, in IS&T Electronic Imaging: Stereoscopic Displays and Applications XXVII Proceedings, San Francisco, CA, USA, 15-17 Febr., 2016.
• F. Seitner, M. Nezveda, M. Gelautz, G. Braun, C. Kapeller, W. Zellinger and B. Moser. Trifocal system for high-quality inter-camera mapping and virtual view synthesis; International Conference on 3D Imaging (IC3D), Liège, Belgium; Dec 14-15, 2015, pages 1-8, DOI 10.1109/IC3D.2015.7391819. 
• B. Moser. Matching Event Sequences Approach based on Weyl's Discrepancy Norm, 1st. Int. Conf. on Event-based Control, Communication, and Signal Processing, EBCCSP 15, June 17-19, Krakow, Poland, 2015,  DOI 10.1109/EBCCSP.2015.7300676.  
• B. Moser. Stability of Threshold-based Sampling as Metric Problem, 1st. Int. Conf. on Event-based Control, Communication, and Signal Processing, Special Session on Mathematical Modeling of Event-based Sampling, EBCCSP 15, June 17-19, Krakow, Poland, 2015, DOI 10.1109/EBCCSP.2015.7300692.
• G. Stübl, P. Haslinger, V. Wieser, J. Scharinger, and B. Moser. Periodicity Estimation of Nearly Regular Textures based on Discrepancy Norm. In Proc. SPIE 8661, Image Processing: Machine Vision Applications VI, pp. 866106--866106-10. Burlingame, California, USA, 2013. 
• G. Stübl, J.-L. Bouchot, P. Haslinger, and B. Moser. Discrepancy Norm as Fitness Function for Defect Detection on Regularly Textured Surfaces. In A. Pinz, T. Pock, H. Bischof, and F. Leberl, editors, Pattern Recognition, volume 7476 of Lecture Notes in Computer Science, pp. 428--437, Springer Berlin Heidelberg, 2012.
• B. Moser, G. Stübl, and J. L. Bouchot. On a Non-Monotonicity Effect of Similarity Measures, In M. Pelillo and E.R. Hancock, editors, 1st Int. Conf. on Similarity-based Pattern Recognition, SIMBAD'11, vol. 7005 of Lecture Notes in Computer Science, pp. 46--60, Springer Berlin Heidelberg, 2011.
• G. Stübl, B. Moser, J. Scharinger. On approximate nearest neighbor field algorithms in template matching for surface quality inspection In A. Quesada-Arencibia et al. (editors), Computer Aided Systems Theory - EUROCAST 2013,  Lecture Notes in Computer Science, vol. 8112,  pp 79-86, 2013.
• V. Wieser, C. Grelck, H. Schöner, P. Haslinger, K. Bosa, B. Moser. GPU-based image processing use cases: A high-level approach. In K. De Bosschere, E.H. D'Hollander, G.R. Joubert, D. Padua, F. Peters (editors). Applications, Tools and Techniques on the Road to Exascale Computing, Advances in Parallel Computing, pages 199-206, IOS Press. 2012.
• V. Wieser, B. Moser, S. Scholz, S. Herhut, J. Guo. Combining high productivity and high performance in image processing using single assignment C. In J.-C. Pinoli, J. Debayle, Y. Gavet, F. Gruy, C. Lambert (editors). Proc. SPIE 8000, 10th International Conference on Quality Control by Artificial Vision (QCAV'2011), DOI: 10.1117/12.890920, Society of Photo-Optical Instrumentation Engineers, 2012.
• J. Bouchot, G. Stübl, B. Moser. A template matching approach based on the discrepancy norm for defect detection on regularly textured surfaces. In J. Pinoli, J. Debayle, Y. Gavet, F. Gruy, C. Lambert (editors), Proc. SPIE 8000, 10th International Conference on Quality Control by Artificial Vision (QCAV'2011), DOI: 10.1117/12.889865, Society of Photo-Optical Instrumentation Engineers, 2012.
• J. Bouchot, J. Himmelbauer, and B. Moser. On autocorrelation based on Hermann Weyl's discrepancy norm for time series analysis. In Proc. 2010 Int. Joint Conf. on Neural Networks (IJCNN'10), pages 1-7, 2010. 
• C. Gonzalez-Mocillo, L. Lopez, J. Castro-Schez, B. Moser. A data-mining approach to 3D realistic render setup assistance. In K. Haigh, N. Rychtyckjy (editors). Proceedings of the 21st Innovative Applications of Artificial Intelligence Conference IAAI 2009, pages 93-98, AAAI Press. 2009.
• H. Schöner, B. Moser, A. Dorrington, A. Payne, M. Cree, B. Heise, F. Bauer. A clustering based denoising technique for range images of time of flight cameras. In M. Masoud Mohammadian (editor). Proc.  Int. Conf. on Computational Intelligence for Modelling, Control and Automation (CIMCA 2008), pages 989-994, IEEE Computer Society. 2008  H. Schöner, B. Moser, E. Lughofer. On preprocessing multi-channel sensor data for online process monitoring. In M. Masoud Mohammadian (editor). Proc. Int. Conf. on Computational Intelligence for Modelling, Control and Automation (CIMCA 2008), pp. 414-419, IEEE Computer Society. 2008.
• B. Moser, P. Haslinger, and T. Kazmar. On the potential of Hermann Weyl's discrepancy norm for texture analysis. In Proc. 2008 Int. Conf. on Computational Intelligence for Modeling, Control and Automation; Intelligent Agents, Web Technologies and Internet Commerce; and Innovation in Software Engineering (CIMCA/IAWTIC/ISE), pages 187-191. IEEE Computer Society, 2008.
• B. Moser and T. Hoch. Misalignment measure based on Hermann Weyl's discrepancy. In Proceedings of 32nd Workshop of the Austrian Association for Pattern Recognition (AAPT/OAGM), Challenges in the Biosciences: Image Analysis and Pattern Recognition Aspects, volume 232, pages 187-198, Linz, Austria, May 2008.
• B. Moser and U. Bodenhofer. Correspondences between fuzzy equivalence relations and kernels: theoretical results and potential applications. In Proc. 15th IEEE Int. Conf. on Fuzzy Systems, pg 10217-10223, Vancouver, July 2006.
• B. Moser and M. Navara. Conditionally Firing Rules Extend the Possibilities of Fuzzy Controllers. In Proc. Int. Conf. Computational Intelligence for Modelling, Control and Automation, pp. 242-245, 1999.
• B. Moser and M. Navara. Which triangular norms are convenient for fuzzy controllers. In Proc. EUSFLAT-ESTYLF Joint Conf. 99, Universitat de les Illes, pp. 75-78, 1999.
   

Contributions to Edited Volumes

• Guest Editor of special issue of “Machine Learning and Application of Mathematical Methods” of MDPI journal (www.mdpi.com/journal/mathematics)V. Wieser, C. Grelck, H. Schöner, P. Haslinger, K. Bosa, B. Moser. GPU-based image processing use cases: A high-level approach. In K. De Bosschere, E.H. D'Hollander, G.R. Joubert, D. Padua, F. Peters (editors). Applications, Tools and Techniques on the Road to Exascale Computing, Advances in Parallel Computing, pages 199-206, IOS Press. 2012.

• W. Beer, W. Kurschl, F.- Matusek, B. Moser, S. Mitsch, S. Sutor: Application Development and Management of Smart Camera Networks. Chapter 14 of “Smart Cameras”, A.N. Belbachier (ed.), Springer Science+Business Media, 2009, pp. 259-266, Springer US, 2009.
• W. Schreiner (ed), K. Bosa, A. Langegger, T. Leitner, B. Moser, S. Pall, V. Wieser, W. Wöß: Parallel, Distributed, and Grid Computing. Chapter VII of Hagenberg Research, B. Buchberger et al (eds), pp. 333-378, Springer, 2009.
• E. P. Klement, E. Lughofer, J. Himmelbauer, B. Moser: Data-Driven and Knowledge-Based Modelling. Chapter V of Hagenberg Research, B. Buchberger et al., Springer, pp. 237-279, Springer 2009.
• P. Bauer, E. P. Klement, B. Moser, and A. Leikermoser. Modeling of Control Functions by Fuzzy Controllers. In H. T. Nguyen, M. Sugeno, R. M. Tong and R. R. Yager, editors, Theoretical Aspects of Fuzzy Control, chapter 5, 91–116. John Wiley & Sons, New York, 1995.
• P. Bauer, E. P. Klement, A. Leikermoser and B. Moser, Interpolation and approximation of real input-output functions using fuzzy rule bases. In R. Kruse, J. Gebhardt, R. Palm, editors, Fuzzy Systems in Computer Science., 245–254, Vieweg, Braunschweig, 1994. 

Theses

• B. Moser. Diodorean modality of the linear time and the Minkowsky space-time. Master’s Thesis, Paris Lodron University Salzburg, October 1991.
• B. Moser. A new approach for representing control surfaces by fuzzy rule bases. PhD Thesis, Johannes Kepler University Linz, February 1996 (mentor Prof. E. P. Klement)
• B. Moser. Novel Similarity and Distance Measures with Applications in Machine Learning, Image and Signal Processing, Habilitation Thesis, Johannes Kepler University Linz, March 2017.

Membership of Commitees and Refereeing Activities

• Co-Organization of DEXA Conference (September 14-17; Bratislava):

  • General Chair of DaWaK2020 (The 22nd International Conference on Big Data Analytics and Knowledge Discovery)
  • Workshop BIOKDD 2020 (The 11th International Workshop on Biological Knowledge Discovery from Big Data)
  • Workshop MLKgraphs 2020 (The 2nd International Workshop on Machine Learning and Knowledge Graphs)
• Co-Organizer of WiseML workshop for ACM WiSec 2020 (with Y. Shi und Y. Sagduyu); 
• Co-General Chair of 5th Conference on Event-Based Control, Communication and Signal Processing (EBCCSP 2019) (with M. Miśkowicz);
• Co-Organizer of workshop on “Knowledge Graph Meets Machine Learning” at DEXA 2019 (and planned 2020 in Bratislava)
• PC Member of EBCCSP and organisation of Special Session on “Mathematical Modelling of Event-based Sampling”, Krakow, Poland, 2015, 2016, Madeira 2017;
• PC Member of 9th International Joint Conference on Computational Intelligence, IJCCI 2017
• PC Member and Special Session Organizer at EBCCSP 2015-19 (Int. Conf. on Event-Based Control, Communication and Signal Processing);
• Program Chair of the Austrian Association for Pattern Recognition 2016, OAGM/AAPR.
• Co-Organisation of OAGM, Program Chair, Wels, Austria, 2016.
• PC Member of QCAV, Lyon, France, 2011.
• PC Member of OAGM, Linz, 2008.

Selected Talks

• Keynote: “AI in Manufacturing: Current Trends and Emerging Technologies” at b2match networking event AI in Manufacturing Vienna-Brno-Bratislava-Linz, 5th of Nov 2019, Vienna;
• Podium „Human Centered AI in Digitized Working Environments“ with R. Sommer (Plattform Industrie 4.0) at Artificial Intelligence Konferenz IMAGINE19, 30th Oct. 2019, Vienna;
• ‘Anonymisierung von Echtdaten zur Anreichung der Lerninhalte für KI-Systeme’ with M. Lichtenthaler (Bundesrechenzentrum, BRZ) at Artificial Intelligence Konferenz IMAGINE19, 30th  of Oct. 2019, Vienna;
• “Bio-Inspired Signal Processing: Quasi-Isometry as Key for Math Framework”, ICT Proposers Day, Sept. 2019, Helsinki;
• “AI on Edge, The ALOHA Contribution”, ICT Proposers Day, Sept. 2019, Helsinki;
• “compAIn: Human AI interaction in manufacturing”, ICT Proposers Day, Sept. 2019, Helsinki;
• “Transfer Learning for Quality Inspection in Manufacturing”,  at Manufacturing Performance Days 2019 (MPD 2019), B2B summit, June 4-6 2019 in Tampere, Finland;
• Keynote: “Integrity of Neural Networks”, 7th Applied AI Meetup Vienna, 23th of May 2019;
• Keynote: “Videoanalyse – Metadaten – Künstliche Intelligenz” at VDV UA-Telematik – Forum-Videotechnik (for German traffic corporations), Köln, 11./12. February 2019; 
• Keynote: “Is AI secure?” at Business & Technology Forum Upper Austria, 30th of Nov. 2018, Linz;

Patents

• German Patent, DE 10 2018 122 019.7, DPMA S2950-DE, on a method to verify the classifier integrity of deep neural networks, submitted 09/2018, B. Moser (inventor); submitted as PCT patent, Oct. 2019;
• European Patent, P14131b, Registration Nr. 161998414, granted 19th of July 2019) on Self-Calibration of Cameras for Velocity Measurements, B. Moser; W. Kloihofer and P. Kniefacz from PKE Electreonics (inventors);
• Austrian Patent, ANR 1415 / 2008; IPC G01N, patent applied for detecting structural defects in patterns with high regularities like woven fabrics, P. Haslinger and B. Moser (inventors), 2008, accepted 2010;
• Austrian Patent, ANR 1131 / 2008; IPC G01B, patent applied for an illumination technique for detecting defects on reflecting surfaces, V. Wieser and B. Moser (inventors), 2008, accepted 2010;

Selection of Reviewing

IEEE Transactions on Signal Processing, IEEE Transactions on Medical Imaging, IEEE Transactions on Fuzzy Systems, Journal of Electronic Imaging, Journal of Fuzzy Sets and Systems, Journal for Soft Computing, Lecture Notes in Computer Science (LNCS), Journal of Mathematical and Computer Modelling, Journal of Applied Mathematics Letters, Journal of Information Science, Mathematical Reviews; Information Sciences.