@inproceedings{salvador_cultural_2015, address = {Boston, Massachusetts, United States}, title = {Cultural {Event} {Recognition} with {Visual} {ConvNets} and {Temporal} {Models}}, url = {http://arxiv.org/abs/1504.06567}, abstract = {This paper presents our contribution to the ChaLearn Challenge 2015 on Cultural Event Classification. The challenge in this task is to automatically classify images from 50 different cultural events. Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical classifier scheme. We extract visual features from the last three fully connected layers of both CaffeNet (pretrained with ImageNet) and our fine tuned version for the ChaLearn challenge. We propose a late fusion strategy that trains a separate low-level SVM on each of the extracted neural codes. The class predictions of the low-level SVMs form the input to a higher level SVM, which gives the final event scores. We achieve our best result by adding a temporal refinement step into our classification scheme, which is applied directly to the output of each low-level SVM. Our approach penalizes high classification scores based on visual features when their time stamp does not match well an event-specific temporal distribution learned from the training and validation data. Our system achieved the second best result in the ChaLearn Challenge 2015 on Cultural Event Classification with a mean average precision of 0.767 on the test set.}, booktitle = {Proceedings of the {CVPR} {Workshop} {ChaLearn} {Looking} at {People} 2015}, publisher = {IEEE}, author = {Salvador, Amaia and Zeppelzauer, Matthias and Manchón-Vizuente, Daniel and Calafell, Andrea and Giró-i-Nieto, Xavier}, month = apr, year = {2015}, keywords = {2015, Center for Artificial Intelligence, Computer Vision, Creative Industries, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Publikationstyp Präsentation, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed, visual computing}, } @inproceedings{zaharieva_social_2015, address = {Shanghai, China}, title = {Social {Event} {Mining} in {Large} {Photo} {Collections}}, abstract = {A significant part of publicly available photos on the Internet depicts a variety of different social events. In order to organize this steadily growing media content and to make it easily accessible, novel indexing methods are required. Essential research questions in this context concern the efficient detection (clustering), classification, and retrieval of social events in large media collections. In this paper we explore two aspects of social events mining. First, the initial clustering of a given photo collection into single events and, second, the retrieval of relevant social events based on user queries. For both aspects we employ commonly available metadata information, such as user, time, GPS data, and user-generated textual descriptions. Performed evaluations in the context of social event detection demonstrate the strong generalization ability of our approach and the potential of contextual data such as time, user, and location. Experiments with social event retrieval clearly indicate the open challenge of mapping between previously detected event clusters and heterogeneous user queries.}, booktitle = {Proceedings of the {International} {Conference} on {Multimedia} {Retrieval}}, publisher = {ACM Press}, author = {Zaharieva, Maia and Zeppelzauer, Matthias and Del Fabro, Manfred and Schopfhauser, Daniel}, month = mar, year = {2015}, keywords = {2015, Center for Artificial Intelligence, Computer Vision, Creative Industries, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Pattern recognition, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed, visual computing}, } @inproceedings{agapito_graph-based_2015, series = {Lecture {Notes} in {Computer} {Science}}, title = {Graph-{Based} {Shape} {Similarity} of {Petroglyphs}}, volume = {8925}, isbn = {978-3-319-16177-8}, url = {http://dx.doi.org/10.1007/978-3-319-16178-5_9}, language = {English}, booktitle = {Computer {Vision} - {ECCV} 2014 {Workshops}}, publisher = {Springer International Publishing}, author = {Seidl, Markus and Wieser, Ewald and Zeppelzauer, Matthias and Pinz, Axel and Breiteneder, Christian}, editor = {Agapito, Lourdes and Bronstein, Michael M. and Rother, Carsten}, year = {2015}, note = {Projekt: PITOTI 3D}, keywords = {2015, Center for Artificial Intelligence, Computer Vision, Creative Industries, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Graph edit distance, Graph embedding, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Petroglyph similarity, Publikationstyp Schriftpublikation, Shape similarity, Vortrag, Wiss. Beitrag, best, best-lbseidl, graph matching, peer-reviewed, visual computing}, pages = {133--148}, } @inproceedings{zeppelzauer_interactive_2015, address = {Granada, Spain}, title = {Interactive {Segmentation} of {Rock}-{Art} in {High}-{Resolution} {3D} {Reconstructions}}, booktitle = {Conference {Proceedings} of {Digital} {Heritage} 2015 {Full} {Papers}}, author = {Zeppelzauer, Matthias and Poier, Georg and Seidl, Markus and Reinbacher, Christian and Breiteneder, Christian and Bischof, Horst}, month = oct, year = {2015}, note = {Projekt: PITOTI 3D}, keywords = {2015, Center for Artificial Intelligence, Computer Vision, Creative Industries, Department Medien und Digitale Technologien, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Publikationstyp Schriftpublikation, Vortrag, Wiss. Beitrag, best, peer-reviewed}, } @inproceedings{dumphart_is_2023, series = {{ESMAC} 2023 {Abstracts}}, title = {Is it time to re-think the appropriateness of autocorrelation for gait event detection? {Preliminary} results of an ongoing study}, volume = {106}, shorttitle = {Is it time to re-think the appropriateness of autocorrelation for gait event detection?}, url = {https://www.sciencedirect.com/science/article/pii/S0966636223009840}, doi = {10.1016/j.gaitpost.2023.07.064}, abstract = {Introduction Recent developments in machine learning (ML)-based gait event detection have demonstrated superior results in terms of robustness and temporal accuracy compared to heuristic approaches [1–3]. “Autocorrelation” (AC) is an implemented heuristic algorithm in the Vicon Nexus application, which identifies events based on a recurring pattern of a certain marker. Clinicians often rely on the use of AC or other heuristic approaches to identify initial contact (IC) and foot off (FO) events. However, no literature exists on the accuracy of AC for event detection. We have recently developed IntellEvent [4], a ML-based event detection algorithm which has superior accuracy compared to current state-of-the-art methods [1,5]. We aim to evaluate its applicability in daily clinical use compared to the AC method. Research question How large are the temporal differences of gait events detected by IntellEvent and the AC method? Methods The retrospective dataset for this study comprises 3DGA data. Patients were classified having either malrotation deformities (MD, n=20) or infantile cerebral palsy (ICP, n=18). IntellEvent was used to detect all IC and FO events using the left and right velocity of the heel, ankle, and toe trajectories. For the AC method a threshold of 20N was used to detect all IC and FO events on force plates. Subsequently, AC was used to detect all other events using the least square method of the x-axis trajectory (direction of motion). We validated IntellEvent by comparing its predictions to events solely identified with force plates. Afterwards, we calculated the differences between the remaining events of IntellEvent and the AC method. Results Mean absolute errors (95\% confidence interval) of IntellEvent compared to the ground truth for IC (MD: 2.4ms (2.1-2.8), ICP: 3.7ms (3.2–4.1)) and FO (MD: 7.5ms (6.8–8.1), ICP: 10.5 (9.5–11.4)) events showed a high temporal accuracy for both pathologies (Fig. 1). The comparison between IntellEvent and the AC events shows greater deviations for IC (MD: 10.1ms (9.6–0.6), ICP: 11.5ms (10.9–12.1)) and FO (MD: 9.3ms (8.8–9.7), ICP: 15.4ms (14.6–16.2)). Fig. 1. Temporal errors between IntellEvent vs. force plate data (blue) and IntellEvent vs. Autocorrelation (orange). Dotted grey lines indicate an error of 6.67ms (= 1 frames) and dotted red lines an error of 26.66ms (= 4 frames). Discussion IntellEvent achieves a very high temporal accuracy and robustness when compared to ground truth data. For the IC events, a high deviation between IntellEvent and AC was observed. Therefore, the results suggest that the AC can potentially introduce errors that may affect clinical decision making. Our preliminary results indicate that AC events need to be used with care when applied to pathological gait patterns and ML-based methods such as IntellEvent could improve the overall accuracy of gait event detection.}, urldate = {2023-09-18}, booktitle = {Gait \& {Posture}}, author = {Dumphart, Bernhard and Slijepcevic, Djordje and Kranz, Andreas and Zeppelzauer, Matthias and Horsak, Brian}, month = sep, year = {2023}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, Department Medien und Digitale Technologien, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Vortrag, Wiss. Beitrag, best, best-bhorsak, peer-reviewed}, pages = {S50--S51}, } @inproceedings{slijepcevic_towards_2023, series = {{ESMAC} 2023 {Abstracts}}, title = {Towards personalized gait rehabilitation: {How} robustly can we identify personal gait signatures with machine learning?}, volume = {106}, copyright = {Copyright}, shorttitle = {Towards personalized gait rehabilitation}, url = {https://www.sciencedirect.com/science/article/pii/S0966636223012523}, doi = {10.1016/j.gaitpost.2023.07.232}, abstract = {Introduction Personalizing gait rehabilitation requires a comprehensive understanding of the unique gait characteristics of an individual patient, i.e., personal gait signature. Utilizing machine learning to classify individuals based on their gait can help to identify gait signatures [1]. This work exemplifies how an explainable artificial intelligence method can identify the most important input features that characterize the personal gait signature. Research question How robust can gait signatures be identified with machine learning and how sensitive are these signatures with respect to the amount of training data per person? Methods We utilized subsets of the AIST Gait Database 2019 [2], the GaitRec dataset [3], and the Gutenberg Gait Database [4] containing bilateral ground reaction forces (GRFs) during level walking at a self-selected speed. Eight GRF samples from each of 2,092 individuals (1,410/680 male/female, 809/1,283 health control/gait disorder, 1,355/737 shod/barefoot) were used for a gait-based person classification with a (linear) support vector machine (SVM). Two randomly selected samples from each individual served as test data. Gait signatures were identified using relevance scores obtained with layer-wise relevance propagation [5]. To assess the robustness of the identified gait signatures, we compared the relevance scores using Pearson’s correlation coefficient between step-wise reduced training data, from k=6 to k=1 training samples per individual. Results For the baseline setup (k=6), the SVM achieved a test classification accuracy of 99.1\% with 36 out of 4184 test samples being misclassified. The results for the setups with reduced training samples are visualized in Fig. 1. Fig. 1: Overview of the experimental results. Discussion A reduction of training samples per individual causes a decrease in classification accuracy (e.g., by 17.7\% in the case of one training sample per individual). The results show that at least five training samples per individual are necessary to achieve a classification accuracy of approximately 99\% for over 2,000 individuals. A similar effect is observed for gait signatures, which also show a slight degradation in robustness as the number of training samples decreases. In some cases, a model trained with less data per individual learns a different gait signature than a model trained with more data. In the test sample with the lowest correlation (see Fig. 1E), we observe a significant deviation in relevance for some input features. However, only 114 test samples (2.7\%) are below a moderate correlation of r=0.4 [6], indicating that gait signatures are quite robust, even when using one training sample per individual. This is supported by a strong median correlation of r=0.71 [6] (and the highest correlation of r=0.96) between the gait signatures. As automatically identified gait signatures seem to be robust, this approach has the potential to serve as a basis for tailoring interventions to each patient’s specific needs.}, urldate = {2023-09-21}, booktitle = {Gait \& {Posture}}, author = {Slijepcevic, Djordje and Horst, Fabian and Simak, Marvin and Schöllhorn, Wolfgang Immanuel and Zeppelzauer, Matthias and Horsak, Brian}, month = sep, year = {2023}, keywords = {Center for Artificial Intelligence, Center for Digital Health and Social Innovation, Department Medien und Digitale Technologien, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, SP CDHSI Motor Rehabilitation, Vortrag, Wiss. Beitrag, best, best-bhorsak, peer-reviewed}, pages = {S192--S193}, } @article{hogan_knowledge_2021, series = {71}, title = {Knowledge {Graphs}}, volume = {54}, doi = {10.1145/3447772}, abstract = {In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.}, number = {4}, urldate = {2020-12-01}, journal = {ACM Computing Surveys}, author = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and d'Amato, Claudia and de Melo, Gerard and Gutierrez, Claudio and Gayo, José Emilio Labra and Kirrane, Sabrina and Neumaier, Sebastian and Polleres, Axel and Navigli, Roberto and Ngomo, Axel-Cyrille Ngonga and Rashid, Sabbir M. and Rula, Anisa and Schmelzeisen, Lukas and Sequeda, Juan and Staab, Steffen and Zimmermann, Antoine}, month = jul, year = {2021}, keywords = {Computer Science - Artificial Intelligence, Computer Science - Databases, Computer Science - Machine Learning, Extern, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Data Intelligence, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, Wiss. Beitrag, best, best-neumaier, peer-reviewed}, pages = {1--37}, } @book{hogan_knowledge_2022, series = {Synthesis lectures on data, semantics and knowledge}, title = {Knowledge graphs}, isbn = {978-1-63639-235-6}, language = {eng}, number = {22}, publisher = {Morgan \& Claypool Publishers}, author = {Hogan, Aidan and Cochez, Michael and Melo, Gerard de and Neumaier, Sebastian}, year = {2022}, keywords = {Center for Artificial Intelligence, Depart Informatik und Security, Forschungsgruppe Data Intelligence, Institut für IT Sicherheitsforschung, Wiss. Beitrag, best, best-neumaier}, } @book{pellegrini_exploring_2021, address = {Vienna}, series = {Nuclear {Energy} {Series}}, title = {Exploring {Semantic} {Technologies} and {Their} {Application} to {Nuclear} {Knowledge} {Management}}, isbn = {978-92-0-108719-5}, url = {https://www.iaea.org/publications/13469/exploring-semantic-technologies-and-their-application-to-nuclear-knowledge-management}, number = {NG-T-6.15}, publisher = {INTERNATIONAL ATOMIC ENERGY AGENCY}, author = {Pellegrini, Tassilo and Beraha, D. and Gladyshev, M. and de Grosbois, J. and Hakopov, Z. and Jehadeesan, R. and Markov, A. and Marmonti, E. and Nenadic, G.}, year = {2021}, keywords = {!!Nicht verwenden!! - Department Digital Business \& Innovation, Center for Artificial Intelligence, Department Digital Business and Innovation, Institute for Innovation Systems, Schriftpublikation, best, peer-reviewed, wiss. Beitrag}, } @inproceedings{eigner_towards_2021, address = {Rhodes, Greece}, title = {Towards {Resilient} {Artificial} {Intelligence}: {Survey} and {Research} {Issues}}, url = {https://ieeexplore.ieee.org/document/9527986}, doi = {10.1109/CSR51186.2021.9527986}, booktitle = {Proceedings of the 2021 {IEEE} {International} {Conference} on {Cyber} {Security} and {Resilience}}, publisher = {IEEE}, author = {Eigner, Oliver and Eresheim, Sebastian and Kieseberg, Peter and Klausner, Lukas Daniel and Pirker, Martin and Priebe, Torsten and Tjoa, Simon and Marulli, Fiammetta and Mercaldo, Francesco and Priebe, Torsten}, year = {2021}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Data Intelligence, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, SP IT Sec System \& Application Security, Vortrag, Wiss. Beitrag, best, peer-reviewed}, pages = {536--542}, } @book{holzinger_5th_2021, series = {{LNCS}}, title = {5th {IFIP} {TC} 5, {TC} 12, {WG} 8.4, {WG} 8.9, {WG} 12.9 {International} {Cross}-{Domain} {Conference}, {CD}-{MAKE} 2021 {Virtual} {Event}, {August} 17–20, 2021 {Proceedings}}, isbn = {978-3-030-84060-0}, url = {https://link.springer.com/book/10.1007/978-3-030-84060-0?utm_medium=referral&utm_source=google_books&utm_campaign=3_pier05_buy_print&utm_content=en_08082017}, number = {12844}, publisher = {Springer}, author = {Holzinger, Andreas and Kieseberg, Peter and Tjoa, A Min and Weippl, Edgar}, month = aug, year = {2021}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, best, closed Access, peer-reviewed}, } @incollection{pellegrini_tassilo_economics_2013, address = {New York}, series = {Data {Mining} and {Knowledge} {Discovery} {Series}}, title = {The {Economics} of {Big} {Data} – {A} {Value} {Perspective} on {State} of the {Art} and {Future} {Trends}}, isbn = {978-1-4665-7837-1}, url = {https://www.taylorfrancis.com/chapters/edit/10.1201/b16014-21/economics-big-data-value-perspective-state-art-future-trends-tassilo-pellegrin}, language = {en}, booktitle = {Big {Data} {Computing}}, publisher = {Chapman and Hall/CRC}, author = {Pellegrini, Tassilo}, year = {2013}, keywords = {Center for Artificial Intelligence, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Creative Media Technologies, Institut für Medienwirtschaft, Institute for Innovation Systems, Publikationstyp Schriftpublikation, Schriftpublikation, Wiss. Beitrag, best, peer-reviewed}, pages = {343--372}, } @incollection{muller-lietzkow_uberlegungen_2020, series = {Medienökonomie}, title = {Überlegungen zur {Governance} von {Open} {Data} – eine institutionenökonomische {Perspektive}}, volume = {13}, isbn = {978-3-7489-0524-0}, url = {https://www.nomos-elibrary.de/index.php?doi=10.5771/9783748905240-111}, urldate = {2021-01-08}, booktitle = {Beyond {Digital}}, publisher = {Nomos Verlagsgesellschaft mbH \& Co. KG}, author = {Pellegrini, Tassilo and Litschka, Michael}, editor = {Müller-Lietzkow, Jörg}, year = {2020}, doi = {10.5771/9783748905240-111}, keywords = {Center for Artificial Intelligence, Department Medien und Digitale Technologien, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Creative Media Technologies, Institut für Medienwirtschaft, Institute for Innovation Systems, Publiktationstyp Schriftpublikation, Wiss. Beitrag, best, closed Access, peer-reviewed}, pages = {111--130}, } @incollection{pellegrini_quality--service_2012, address = {Baden-Baden}, title = {Quality-of-{Service} in der {Netzbewirtschaftung} – {Effekte} einer eingeschränkten {Netzneutralität} auf {Cloud}-basierte {Geschäftsmodelle}}, isbn = {978-3-8329-6882-3}, language = {de}, booktitle = {Netzneutralität und {Netzbewirtschaftung}}, publisher = {Nomos Verlag}, author = {Pellegrini, Tassilo}, year = {2012}, keywords = {Center for Artificial Intelligence, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Creative Media Technologies, Institut für Medienwirtschaft, Institute for Innovation Systems, Publikationstyp Schriftpublikation, best, cloud computing, net neutrality, peer-reviewed}, pages = {117--137}, } @article{zeppelzauer_interactive_2016, title = {Interactive {3D} {Segmentation} of {Rock}-{Art} by {Enhanced} {Depth} {Maps} and {Gradient} {Preserving} {Regularization}}, volume = {9}, issn = {1556-4673}, url = {https://publik.tuwien.ac.at/files/publik_258520.pdf}, doi = {10/ghpp2n}, number = {4}, journal = {ACM Journal on Computing and Cultural Heritage}, author = {Zeppelzauer, Matthias and Poier, Georg and Seidl, Markus and Reinbacher, Christian and Schulter, Samuel, Christian and Breiteneder, C. and Bischof, Horst}, month = jul, year = {2016}, note = {Article 19 Projekt: PITOTI 3D}, keywords = {Center for Artificial Intelligence, Computer Vision, Department Medien und Digitale Technologien, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, peer-reviewed}, pages = {19:1--19:30}, } @inproceedings{slijepcevic_ground_2017, address = {Trondheim, Norway}, title = {Ground reaction force measurements for gait classification tasks: {Effects} of different {PCA}-based representations}, volume = {57}, url = {http://www.gaitposture.com/article/S0966-6362(17)30712-9/pdf}, doi = {10.1016/j.gaitpost.2017}, booktitle = {Gait \& {Posture} {Supplement}}, author = {Slijepcevic, Djordje and Horsak, Brian and Schwab, Caterine and Raberger, Anna-Maria and Schüller, Michael and Baca, Arnold and Breitender, Christian and Zeppelzauer, Matthias}, year = {2017}, note = {Projekt: IntelliGait Projekt: DHLab}, keywords = {2017, Biofeedback, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Creative Industries, DHLab, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Pattern recognition, Publikationstyp Präsentation, Publikationstyp Schriftpublikation, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, project\_carma, project\_intelligait, ⚠️ Invalid DOI}, pages = {4--5}, } @article{horsak_gaitrec_2020, title = {{GaitRec}, a large-scale ground reaction force dataset of healthy and impaired gait}, volume = {7:143}, copyright = {CC BY}, url = {https://www.nature.com/articles/s41597-020-0481-z}, doi = {10/gh372d}, number = {1}, journal = {Scientific Data}, author = {Horsak, Brian and Slijepcevic, Djordje and Raberger, Anna-Maria and Schwab, Caterine and Worisch, Marianne and Zeppelzauer, Matthias}, year = {2020}, note = {Projekt: I3D Projekt: IntelliGait Projekt: DHLab}, keywords = {2019, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, Eintrag überprüfen, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Green OA, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Open Access, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, best-mzeppelzauer, peer-reviewed, submitted}, pages = {1--8}, } @inproceedings{schwab_intelligait_2018, address = {Hamburg, Deutschland}, title = {{IntelliGait}: {Automatische} {Gangmusteranalyse} für die robuste {Erkennung} von {Gangstörungen}}, booktitle = {Tagungsband des 2ten {GAMMA} {Kongress} ({Gesellschaft} für die {Analyse} {Menschlicher} {Motorik} in ihrer klinischen {Anwendung})}, author = {Schwab, Caterine and Slijepcevic, Djordje and Zeppelzauer, Matthias and Raberger, Anna-Maria and Dumphart, Bernhard and Baca, Arnold and Breitender, Christian and Horsak, Brian}, year = {2018}, note = {Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Creative Industries, DHLab, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Pattern recognition, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, ⛔ No DOI found}, } @inproceedings{horsak_explainable_2020, address = {München, Deutschland}, title = {Explainable {Artificial} {Intelligence} ({XAI}) und ihre {Anwendung} auf {Klassifikationsprobleme} in der {Ganganalyse}}, booktitle = {Abstractband des 3. {GAMMA} {Kongress}}, author = {Horsak, Brian and Dumphart, Bernhard and Slijepcevic, Djordje and Zeppelzauer, Matthias}, year = {2020}, note = {Projekt: ReMoCap-Lab Projekt: DHLab Projekt: I3D}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, Eintrag überprüfen, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Green OA, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Publikationstyp Präsentation, Publikationstyp Schriftpublikation, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Vortrag, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, ⛔ No DOI found}, } @article{horst_explaining_2020, title = {Explaining automated gender classification of human gait}, volume = {81, supplement 1}, url = {http://www.sciencedirect.com/science/article/pii/S0966636220303568}, doi = {10/ghr9k6}, language = {en}, urldate = {2020-09-14}, journal = {Gait \& Posture}, author = {Horst, F. and Slijepcevic, D. and Zeppelzauer, M. and Raberger, A. M. and Lapuschkin, S. and Samek, W. and Schöllhorn, W. I. and Breiteneder, C. and Horsak, B.}, year = {2020}, note = {Projekt: ReMoCap-Lab Projekt: I3D}, keywords = {2020, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, Department Gesundheit, Digital Health, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Institutional Access, Machine Learning, Media Computing Group, Poster, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Vortrag, Wiss. Beitrag, best, best-bhorsak, peer-reviewed}, pages = {159--160}, } @article{despotovic_prediction_2019, title = {Prediction and analysis of heating energy demand for detached houses by computer vision}, volume = {193}, issn = {0360-5442}, url = {https://www.sciencedirect.com/science/article/pii/S0378778818336430?via%3Dihub}, doi = {10/fsxn}, abstract = {Exterior images of real estate contain a large number of visual clues which allow conclusions about the heating energy demand (HED) of a building. Up to now, HED has been determined by specially trained experts such as architects, civil engineers, etc. either on the basis of consumption data or estimated demand values. In this article, we present a novel approach to determine the HED of detached houses. Our suggested approach is based solely on the visual appearance and assumes that exterior images of a building contain a variety of information that allows inferences about the HED of a building. For this, we use the powerful techniques of image analysis and computer vision which are already successfully used in different domains like surveillance, image search, and robotics. The results show that our approach works well and in addition to the HED, the construction period of a building can also be determined. Our algorithm achieves a classification accuracy of 62\% for HED and 57\% for construction age epoch.}, journal = {Energy \& Buildings}, author = {Despotovic, Miroslav and Koch, David and Leiber, Sascha and Döller, Mario and Sakeena, Muntaha and Zeppelzauer, Matthias}, year = {2019}, note = {Projekt: ImmBild Projekt: ImmoAge}, keywords = {Center for Artificial Intelligence, Computer Vision, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Visual Computing, Wiss. Beitrag, best, peer-reviewed}, pages = {29--35}, } @inproceedings{zeppelzauer_sonicontrol_2018, address = {Seoul, South Korea}, title = {{SoniControl} - {A} {Mobile} {Ultrasonic} {Firewall}}, url = {https://arxiv.org/abs/1807.07617}, doi = {10/gh377f}, abstract = {The exchange of data between mobile devices in the near-ultrasonic frequency band is a new promising technology for near field communication (NFC) but also raises a number of privacy concerns. We present the first ultrasonic firewall that reliably detects ultrasonic communication and provides the user with effective means to prevent hidden data exchange. This demonstration showcases a new media-based communication technology ("data over audio") together with its related privacy concerns. It enables users to (i) interactively test out and experience ultrasonic information exchange and (ii) shows how to protect oneself against unwanted tracking.}, urldate = {2018-10-10}, booktitle = {Proceedings of the {ACM} {International} {Conference} on {Multimedia}}, publisher = {ACM Press}, author = {Zeppelzauer, Matthias and Ringot, Alexis and Taurer, Florian}, year = {2018}, note = {arXiv: 1807.07617}, keywords = {Acoustic Cookies, Acoustic Firewall, Acoustic Tracking, Center for Artificial Intelligence, Computer Science - Cryptography and Security, Computer Science - Multimedia, FH SP Cyber Security, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Ultrasound Data Transmission, Wiss. Beitrag, best, best-aringot, peer-reviewed}, } @book{holzinger_6th_2022, series = {{LNCS}}, title = {6th {IFIP} {TC} 5, {TC} 12, {WG} 8.4, {WG} 8.9, {WG} 12.9 {International} {Cross}-{Domain} {Conference}, {CD}-{MAKE} 2022 {Vienna}, {August} 23–26, 2022 {Proceedings}}, isbn = {978-3-031-14462-2}, url = {https://link.springer.com/content/pdf/10.1007/978-3-031-14463-9.pdf}, number = {13480}, publisher = {Springer}, author = {Holzinger, Andreas and Kieseberg, Peter and Tjoa, A Min and Weippl, Edgar}, month = aug, year = {2022}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Data Intelligence, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, best, closed Access, peer-reviewed}, } @inproceedings{kovarova-simecek_adoption_2016, address = {St. Pölten, Austria}, title = {Adoption of {Technical} {Reporting} {Standards} {Among} {Austrian} {Listed} {Companies} - {The} {Case} of {XBRL}}, url = {http://ceur-ws.org/Vol-1734/}, language = {en}, booktitle = {Proceedings of the 9th {Forum} {Media} {Technology} 2016 and 2nd {All} {Around} {Audio} {Symposium} 2016}, publisher = {CEUR WS}, author = {Kovarova-Simecek, Monika and Pellegrini, Tassilo}, year = {2016}, keywords = {BlogFincom, Center for Artificial Intelligence, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Medienwirtschaft, Publikationstyp Schriftpublikation, best, peer-reviewed, ⛔ No DOI found}, } @article{litschka_considerations_2019, title = {Considerations on the {Governance} of {Open} {Data} – an {Institutional} {Economic} {Perspective}}, volume = {9}, issn = {1478-9647, 1478-9655}, url = {http://www.inderscience.com/link.php?id=103028}, doi = {10/gnk9k3}, language = {en}, number = {3/4}, urldate = {2019-10-28}, journal = {International Journal of Intellectual Property Management}, author = {Litschka, Michael and Pellegrini, Tassilo}, year = {2019}, keywords = {Center for Artificial Intelligence, Department Medien und Digitale Technologien, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Creative Media Technologies, Institut für Medienwirtschaft, Institute for Innovation Systems, SP MW Global Media Markets \& Local Practices, Wiss. Beitrag, best, best-lblitschka, best-lbpellegrini, peer-reviewed}, pages = {247--263}, } @article{holzinger_kandinsky_2020, title = {{KANDINSKY} {Patterns}: {A} {Swiss}-{Knife} for the {Study} of {Explainable} {AI}}, url = {https://phaidra.fhstp.ac.at/o:4336}, number = {120}, journal = {ERCIM-News}, author = {Holzinger, Andreas and Kieseberg, Peter and Müller, Heimo}, month = jan, year = {2020}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, best, ⛔ No DOI found}, pages = {41--42}, } @incollection{ege_lizenzierung_2015, address = {Berlin, Heidelberg}, title = {Lizenzierung und {Nutzung} vernetzter {Daten} – {Fallstricke} und {Empfehlungen}}, isbn = {978-3-642-54885-7 978-3-642-54886-4}, url = {http://link.springer.com/10.1007/978-3-642-54886-4_26}, urldate = {2015-11-10}, booktitle = {Corporate {Semantic} {Web}}, publisher = {Springer Berlin Heidelberg}, author = {Pellegrini, Tassilo}, editor = {Ege, Börteçin and Humm, Bernhard and Reibold, Anatol}, year = {2015}, keywords = {Center for Artificial Intelligence, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Creative Media Technologies, Institut für Medienwirtschaft, Institute for Innovation Systems, Publikationstyp Schriftpublikation, best, peer-reviewed}, pages = {381--396}, } @inproceedings{longo_explainable_2020, address = {Virtuell}, title = {Explainable {Artificial} {Intelligence}: {Concepts}, {Applications}, {Research} {Challenges} and {Visions}}, author = {Longo, Luca and Goebel, Randy and Lecue, Freddy and Kieseberg, Peter and Holzinger, Andreas}, month = aug, year = {2020}, keywords = {Center for Artificial Intelligence, Eintrag überprüfen, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Secure Societies, Green OA, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, Wiss. Beitrag, best, closed Access, peer-reviewed}, } @inproceedings{amiri_machine_2018, address = {Porto, Portugal}, title = {A {Machine} {Learning} {Approach} for {Privacy}-preservation in {E}-business {Applications}:}, isbn = {978-989-758-319-3}, shorttitle = {A {Machine} {Learning} {Approach} for {Privacy}-preservation in {E}-business {Applications}}, url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006826304430452}, doi = {10/gh38cd}, language = {en}, urldate = {2019-01-23}, booktitle = {Proceedings of the 15th {International} {Joint} {Conference} on e-{Business} and {Telecommunications}}, publisher = {SCITEPRESS - Science and Technology Publications}, author = {Amiri, Fatemeh and Quirchmayr, Gerald and Kieseberg, Peter}, year = {2018}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, SP IT Sec Security Management \& Privacy, best, peer-reviewed}, pages = {443--452}, } @article{zeppelzauer_study_2018, title = {A {Study} on {Topological} {Descriptors} for the {Analysis} of {3D} {Surface} {Texture}}, volume = {167}, issn = {1077-3142}, url = {https://arxiv.org/pdf/1710.10662}, doi = {10/ghpp2h}, abstract = {Methods from computational topology are becoming more and more popular in computer vision and have shown to improve the state-of-the-art in several tasks. In this paper, we investigate the applicability of topological descriptors in the context of 3D surface analysis for the classification of different surface textures. We present a comprehensive study on topological descriptors, investigate their robustness and expressiveness and compare them with state-of-the-art methods. Results show that class-specific information is reflected well in topological descriptors. The investigated descriptors can directly compete with non-topological descriptors and capture orthogonal information. Moreover they improve the state-of-the-art in combination with non-topological descriptors.}, journal = {Journal on Computer Vision and Image Understanding (CVIU)}, author = {Zeppelzauer, Matthias and Zielinski, Bartosz and Juda, Mateusz and Seidl, Markus}, year = {2018}, note = {Projekt: PITOTI 3D}, keywords = {3D surface classification, Center for Artificial Intelligence, Computer Vision, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Surface texture analysis, Visual Computing, Wiss. Beitrag, best, best-lbseidl, best-mzeppelzauer, peer-reviewed, persistence diagram, persistence image, persistent homology, surface representation, surface topology analysis}, pages = {74 -- 88}, } @article{bernard_jurgen_comparing_2017, title = {Comparing {Visual}-{Interactive} {Labeling} with {Active} {Learning}: {An} {Experimental} {Study}}, volume = {24}, issn = {1077-2626}, url = {http://eprints.cs.univie.ac.at/5257/1/bernard2017labeling.pdf}, doi = {10/gcqb3r}, number = {1}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)}, author = {{Bernard, Jürgen} and Hutter, Marco and Zeppelzauer, Matthias and Fellner, Dieter and Sedlmair, Michael}, year = {2017}, keywords = {2017, Center for Artificial Intelligence, Creative Industries, Department Medien und Digitale Technologien, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed}, } @book{holzinger_machine_2020, series = {{LNCS}}, title = {Machine {Learning} and {Knowledge} {Extraction}: {Fourth} {IFIP} {TC} 5, {TC} 8/{WG} 8.4, 8.9, {TC} 12/{WG} 12.9 {International} {Cross}-{Domain} {Conference}, {CD}-{MAKE} 2020}, url = {https://link.springer.com/book/10.1007/978-3-030-57321-8}, number = {12279}, publisher = {Springer}, author = {Holzinger, Andreas and Kieseberg, Peter and Tjoa, A Min and Weippl, Edgar}, month = aug, year = {2020}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, best, closed Access, peer-reviewed}, } @incollection{holzinger_current_2018, address = {Cham}, title = {Current {Advances}, {Trends} and {Challenges} of {Machine} {Learning} and {Knowledge} {Extraction}: {From} {Machine} {Learning} to {Explainable} {AI}}, volume = {11015}, isbn = {978-3-319-99739-1 978-3-319-99740-7}, shorttitle = {Current {Advances}, {Trends} and {Challenges} of {Machine} {Learning} and {Knowledge} {Extraction}}, url = {http://link.springer.com/10.1007/978-3-319-99740-7_1}, abstract = {In this short editorial we present some thoughts on present and future trends in Artificial Intelligence (AI) generally, and Machine Learning (ML) specifically. Due to the huge ongoing success in machine learning, particularly in statistical learning from big data, there is rising interest of academia, industry and the public in this field. Industry is investing heavily in AI, and spin-offs and start-ups are emerging on an unprecedented rate. The European Union is allocating a lot of additional funding into AI research grants, and various institutions are calling for a joint European AI research institute. Even universities are taking AI/ML into their curricula and strategic plans. Finally, even the people on the street talk about it, and if grandma knows what her grandson is doing in his new start-up, then the time is ripe: We are reaching a new AI spring. However, as fantastic current approaches seem to be, there are still huge problems to be solved: the best performing models lack transparency, hence are considered to be black boxes. The general and worldwide trends in privacy, data protection, safety and security make such black box solutions difficult to use in practice. Specifically in Europe, where the new General Data Protection Regulation (GDPR) came into effect on May, 28, 2018 which affects everybody (right of explanation). Consequently, a previous niche field for many years, explainable AI, explodes in importance. For the future, we envision a fruitful marriage between classic logical approaches (ontologies) with statistical approaches which may lead to context-adaptive systems (stochastic ontologies) that might work similar as the human brain.}, language = {en}, urldate = {2019-01-23}, booktitle = {Machine {Learning} and {Knowledge} {Extraction}}, publisher = {Springer International Publishing}, author = {Holzinger, Andreas and Kieseberg, Peter and Weippl, Edgar and Tjoa, A Min}, editor = {Holzinger, Andreas and Kieseberg, Peter and Tjoa, A Min and Weippl, Edgar}, year = {2018}, doi = {10.1007/978-3-319-99740-7_1}, keywords = {Center for Artificial Intelligence, Center for Digital Health Innovation, FH SP Cyber Security, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, best, peer-reviewed}, pages = {1--8}, } @article{dumphart_robust_2023, title = {Robust deep learning-based gait event detection across various pathologies}, volume = {18}, copyright = {CC-BY}, issn = {1932-6203}, url = {https://dx.plos.org/10.1371/journal.pone.0288555}, doi = {10.1371/journal.pone.0288555}, abstract = {The correct estimation of gait events is essential for the interpretation and calculation of 3D gait analysis (3DGA) data. Depending on the severity of the underlying pathology and the availability of force plates, gait events can be set either manually by trained clinicians or detected by automated event detection algorithms. The downside of manually estimated events is the tedious and time-intensive work which leads to subjective assessments. For automated event detection algorithms, the drawback is, that there is no standardized method available. Algorithms show varying robustness and accuracy on different pathologies and are often dependent on setup or pathology-specific thresholds. In this paper, we aim at closing this gap by introducing a novel deep learning-based gait event detection algorithm called IntellEvent , which shows to be accurate and robust across multiple pathologies. For this study, we utilized a retrospective clinical 3DGA dataset of 1211 patients with four different pathologies (malrotation deformities of the lower limbs, club foot, infantile cerebral palsy (ICP), and ICP with only drop foot characteristics) and 61 healthy controls. We propose a recurrent neural network architecture based on long-short term memory (LSTM) and trained it with 3D position and velocity information to predict initial contact (IC) and foot off (FO) events. We compared IntellEvent to a state-of-the-art heuristic approach and a machine learning method called DeepEvent. IntellEvent outperforms both methods and detects IC events on average within 5.4 ms and FO events within 11.3 ms with a detection rate of ≥ 99\% and ≥ 95\%, respectively. Our investigation on generalizability across laboratories suggests that models trained on data from a different laboratory need to be applied with care due to setup variations or differences in capturing frequencies.}, language = {en}, number = {8}, urldate = {2023-08-17}, journal = {PLOS ONE}, author = {Dumphart, Bernhard and Slijepcevic, Djordje and Zeppelzauer, Matthias and Kranzl, Andreas and Unglaube, Fabian and Baca, Arnold and Horsak, Brian}, editor = {Srinivasan, Kathiravan}, month = aug, year = {2023}, keywords = {Artificial intelligence, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, Department Medien und Digitale Technologien, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine learning, Phaidra, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Wiss. Beitrag, best, best-bdumphart, peer-reviewed}, pages = {e0288555}, } @inproceedings{slijepcevic_towards_2023, address = {Heidelberg}, series = {{GAMMA} 2023 {Abstracts}}, title = {Towards more transparency: {The} utility of {Grad}-{CAM} in tracing back deep learning based classification decisions in children with cerebral palsy}, volume = {100}, copyright = {Copyright}, shorttitle = {Towards more transparency}, url = {https://www.sciencedirect.com/science/article/pii/S0966636222006828}, doi = {10.1016/j.gaitpost.2022.11.045}, abstract = {GAMMA Conference}, language = {en}, urldate = {2023-03-10}, booktitle = {Gait \& {Posture}}, author = {Slijepcevic, Djordje and Zeppelzauer, Matthias and Unglaube, Fabian and Kranzl, Andreas and Breiteneder, Christian and Horsak, Brian}, month = mar, year = {2023}, note = {Projekt: I3D}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health and Social Innovation, Department Gesundheit, Department Medien und Digitale Technologien, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, SP CDHSI Motor Rehabilitation, Vortrag, Wiss. Beitrag, best, peer-reviewed}, pages = {32--33}, } @article{wagner_kavagait_2018, title = {{KAVAGait}: {Knowledge}-{Assisted} {Visual} {Analytics} for {Clinical} {Gait} {Analysis}}, volume = {25}, url = {https://doi.org/10.1109/TVCG.2017.2785271}, doi = {10/ghppzn}, abstract = {In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient’s gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.}, number = {3}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)}, author = {Wagner, Markus and Slijepcevic, Djordje and Horsak, Brian and Rind, Alexander and Zeppelzauer, Matthias and Aigner, Wolfgang}, year = {2018}, note = {Projekt: KAVA-Time Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, Design Study, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Digital Technologies, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Healthcare, Human Gait Analysis, Human-Computer Interaction, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Visual analytics, Wiss. Beitrag, best, best-bhorsak, best-lbaigner, best-lbwagnerm, best-mzeppelzauer, information visualization, knowledge generation, peer-reviewed}, pages = {1528--1542}, } @article{zeppelzauer_study_2016, title = {A {Study} on {Topological} {Descriptors} for the {Analysis} of {3D} {Surface} {Texture}}, abstract = {Methods from computational topology are becoming more and more popular in computer vision and have shown to improve the state-of-the-art in several tasks. In this paper, we investigate the applicability of topological descriptors in the context of 3D surface analysis for the classification of different surface textures. We present a comprehensive study on topological descriptors, investigate their robustness and expressiveness and compare them with state-of-the-art methods. Results show that class-specific information is reflected well in topological descriptors. The investigated descriptors can directly compete with non-topological descriptors and capture orthogonal information. Moreover they improve the state-of-the-art in combination with non-topological descriptors.}, journal = {Journal on Computer and System Sciences}, author = {Zeppelzauer, Matthias and Zielinski, Bartosz and Juda, Mateusz and Seidl, Markus}, year = {2016}, note = {Projekt: PITOTI 3D}, keywords = {2016, 3D surface classification, Center for Artificial Intelligence, Department Medien und Digitale Technologien, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Media Computing Group, Publikationstyp Schriftpublikation, SP, Surface texture analysis, Wiss. Beitrag, best, best-lbseidl, peer-reviewed, persistence diagram, persistence image, persistent homology, surface representation, surface topology analysis, ⛔ No DOI found}, pages = {60}, } @article{slijepcevic_input_2020, title = {Input {Representations} and {Classification} {Strategies} for {Automated} {Human} {Gait} {Analysis}}, volume = {76}, issn = {0966-6362}, doi = {10/ghz24x}, journal = {Gait \& Posture}, author = {Slijepcevic, Djordje and Zeppelzauer, Matthias and Schwab, Caterine and Raberger, Anna-Maria and Breitender, Christian and Horsak, Brian}, year = {2020}, note = {Projekt: IntelliGait Projekt: I3D Projekt: ReMoCap-Lab Projekt: DHLab}, keywords = {2020, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, Eintrag überprüfen, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Green OA, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Open Access, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, peer-reviewed}, pages = {198--203}, } @article{slijepcevic_automatic_2018, title = {Automatic {Classification} of {Functional} {Gait} {Disorders}}, volume = {5}, issn = {2168-2194}, url = {https://arxiv.org/abs/1712.06405}, doi = {10/ghz24w}, number = {22}, urldate = {2017-12-21}, journal = {IEEE Journal of Biomedical and Health Informatics}, author = {Slijepcevic, Djordje and Zeppelzauer, Matthias and Raberger, Anna-Maria and Schwab, Caterine and Schuller, Michael and Baca, Arnold and Breiteneder, Christian and Horsak, Brian}, year = {2018}, note = {Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, best-mzeppelzauer, peer-reviewed}, pages = {1653 -- 1661}, } @inproceedings{slijepcevic_usefulness_2019, address = {Vienna, Austria}, title = {On the usefulness of statistical parameter mapping for feature selection in automated gait classification}, booktitle = {Book of {Abstracts} of the 25th {Conference} of the {European} {Society} of {Biomechanics} ({ESB})}, author = {Slijepcevic, Djordje and Raberger, Anna-Maria and Zeppelzauer, Matthias and Dumphart, Bernhard and Breiteneder, Christian and Horsak, Brian}, year = {2019}, note = {Projekt: IntelliGait Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Digital Health, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Vortrag, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, ⛔ No DOI found}, pages = {1}, } @inproceedings{slijepcevic_towards_2018, address = {Prague, Czech Republic}, title = {Towards an optimal combination of input signals and derived representations for gait classification based on ground reaction force measurements.}, volume = {65}, doi = {10/gh38wn}, booktitle = {Gait \& {Posture} {Supplement}}, author = {Slijepcevic, Djordje and Zeppelzauer, Matthias and Schwab, Caterine and Raberger, Anna-Maria and Dumphart, B and Baca, Arnold and Breiteneder, Christian and Horsak, Brian}, year = {2018}, note = {Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health and Social Innovation, Classification, DHLab, FH SP Data Analytics \& Visual Computing, Feature Representations, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Gait Recognition, Human Gait Analysis, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, PCA, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, SVM, Wiss. Beitrag, best, best-bhorsak, pattern recognition, peer-reviewed}, } @inproceedings{slijepcevic_usefullness_2019, address = {Vienna, Austria}, title = {On the usefullness of statistical parameter mapping for feature selection in automated gait classification}, booktitle = {Book of {Abstracts} of the 25th {Conference} of the {European} {Society} of {Biomechanics} ({ESB})}, author = {Slijepcevic, Djordje and Raberger, Anna-Maria and Zeppelzauer, Matthias and Dumphart, Bernhard and Breiteneder, Christian and Horsak, Brian}, year = {2019}, note = {Projekt: IntelliGait Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health and Social Innovation, DHLab, Digital Health, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Vortrag, Wiss. Beitrag, best, peer-reviewed, ⛔ No DOI found}, pages = {1}, } @inproceedings{iber_mind_2021, title = {Mind the {Steps}: {Towards} {Auditory} {Feedback} in {Tele}-{Rehabilitation} {Based} on {Automated} {Gait} {Classification}}, doi = {10/gnt2tc}, abstract = {We describe a proof-of-concept for the implementation of a mobile auditory biofeedback system based on automated classification of functional gait disorders. The classification is embedded in a sensor-instrumented insole and is based on ground reaction forces (GRFs). GRF data have been successfully used for the classification of gait patterns into clinically relevant classes and are frequently used in clinical practice to quantitatively describe human motion. A feed-forward neural network that was implemented on the firmware of the insole is used to estimate the GRFs using pressure and accelerator data. Compared to GRF measurements obtained from force plates, the estimated GRFs performed highly accurately. To distinguish between physiological gait and gait disorders, we trained and evaluated a support vector machine with labeled data from a publicly accessible database. The automated gait classification was sonified for auditory feedback. The high potential of the implemented auditory feedback for preventive and supportive applications in physical therapy, such as supervised therapy settings and tele-rehabilitation, was highlighted by a semi- structured interview with two experts.}, booktitle = {In {Proceedings} of the 16th {International} {Audio} {Mostly} {Conference} ({AM}’21)}, publisher = {ACM}, author = {Iber, Michael and Dumphart, Bernhard and Oliveira, Victor A. de. J. and Ferstl, Stefan and Reis, Joschua and Slijepcevic, Djordje and Heller, Mario and Raberger, Anna-Maria and Horsak, Brian}, year = {2021}, note = {Projekt: Sonigait II}, keywords = {Artificial Intelligence, Biofeedback, Biomechanics, CDHI, Digital Health, Forschungsgruppe Media Computing, Gait Analysis, Human-computer interaction, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Motor rehabilitation, Vortrag, Wiss. Beitrag, best, best-lbiber, peer-reviewed}, } @article{bernard_vial_2018, title = {{VIAL} – {A} {Unified} {Process} for {Visual}-{Interactive} {Labeling}}, volume = {34}, copyright = {Springer, Berlin, Heidelberg}, issn = {1432-2315}, url = {https://bit.ly/2My1Yrt}, doi = {10/gd5hr3}, abstract = {The assignment of labels to data instances is a fundamental prerequisite for many machine learning tasks. Moreover, labeling is a frequently applied process in visual-interactive analysis approaches and visual analytics. However, the strategies for creating labels usually differ between these two fields. This raises the question whether synergies between the different approaches can be attained. In this paper, we study the process of labeling data instances with the user in the loop, from both the machine learning and visual-interactive perspective. Based on a review of differences and commonalities, we propose the ’Visual-Interactive Labeling‘ (VIAL) process that unifies both approaches. We describe the six major steps of the process and discuss their specific challenges. Additionally, we present two heterogeneous usage scenarios from the novel VIAL perspective, one on metric distance learning and one on object detection in videos. Finally, we discuss general challenges to VIAL and point out necessary work for the realization of future VIAL approaches.}, number = {1189}, journal = {The Visual Computer}, author = {Bernard, Jürgen and Zeppelzauer, Matthias and Sedlmair, Michael and Aigner, Wolfgang}, year = {2018}, note = {Projekt: KAVA-Time Projekt: IntelliGait Projekt: CARMA}, keywords = {Active Learning, Candidate Selection, Center for Artificial Intelligence, Creative Industries, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Interactive Labeling, Labeling Strategies, Machine Learning, Media Computing Group, Visual Interactive Labeling, best, best-mzeppelzauer, information visualization}, pages = {16}, } @article{stoger_legal_2021, title = {Legal aspects of data cleansing in medical {AI}}, volume = {42}, copyright = {Open Access}, url = {https://www.sciencedirect.com/science/article/pii/S0267364921000601}, doi = {https://doi.org/10.1016/j.clsr.2021.105587}, abstract = {Data quality is of paramount importance for the smooth functioning of modern data-driven AI applications with machine learning as a core technology. This is also true for medical AI, where malfunctions due to "dirty data" can have particularly dramatic harmful implications. Consequently, data cleansing is an important part in improving the usability of (Big) Data for medical AI systems. However, it should not be overlooked that data cleansing can also have negative effects on data quality if not performed carefully. This paper takes an interdisciplinary look at some of the technical and legal challenges of data cleansing against the background of European medical device law, with the key message that technical and legal aspects must always be considered together in such a sensitive context.}, journal = {Computer Law \& Security Review}, author = {Stöger, Karl and Schneeberger, David and Kieseberg, Peter and Holzinger, Andreas}, month = jan, year = {2021}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Data Intelligence, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, best, best pkieseberg, peer-reviewed}, } @article{bernard_jurgen_taxonomy_2021, title = {A {Taxonomy} of {Property} {Measures} to {Unify} {Active} {Learning} and {Human}-centered {Approaches} to {Data} {Labeling}}, volume = {11}, copyright = {Open Access}, issn = {2160-6455}, url = {https://dl.acm.org/doi/abs/10.1145/3439333}, doi = {10/gnt2wf}, number = {3-4}, journal = {ACM Transactions on Interactive Intelligent Systems (TiiS)}, author = {{Bernard, Jürgen} and Hutter, Marco and Sedlmair, Michael and {Zeppelzauer, Matthias} and {Munzner, Tamara}}, year = {2021}, note = {Projekt: BigDataAnalytics Projekt: I3D Projekt: PlantAI}, keywords = {2020, Center for Artificial Intelligence, Department Medien und Digitale Technologien, Eintrag überprüfen, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Media Computing Group, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed}, pages = {1--42}, } @article{kovarova-simecek_financial_2016, title = {Financial {Reporting} {Trends} and {Adoption} of {XBRL} among {Austrian} {Listed} {Companies}}, volume = {7}, issn = {2029-431X}, number = {1}, journal = {Studies of Modern Society - Academic Papers}, author = {Kovarova-Simecek, Monika and Pellegrini, Tassilo}, year = {2016}, keywords = {BlogFincom, Center for Artificial Intelligence, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Medienwirtschaft, Publikationstyp Schriftpublikation, best, peer-reviewed, ⛔ No DOI found}, pages = {240--250}, } @article{schacht_analysis_2020, title = {An {Analysis} of 5 {Million} {OpenPGP} {Keys}}, volume = {11}, url = {http://isyou.info/jowua/papers/jowua-v11n3-6.pdf}, number = {3}, journal = {Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA)}, author = {Schacht, Birger and Kieseberg, Peter}, month = jan, year = {2020}, note = {Projekt: Blockchain Security}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, Open Access, SP IT Sec System \& Application Security, best, peer-reviewed, ⛔ No DOI found}, pages = {107--140}, } @inproceedings{bernard_towards_2018, address = {Brno, Czech Republic}, title = {Towards {User}-{Centered} {Active} {Learning} {Algorithms}}, volume = {37}, url = {http://doi.wiley.com/10.1111/cgf.13406}, doi = {10/gdw79h}, language = {en}, urldate = {2018-10-10}, booktitle = {Computer {Graphics} {Forum}}, author = {Bernard, Jürgen and Zeppelzauer, Matthias and Lehmann, Markus and Müller, Martin and Sedlmair, Michael}, year = {2018}, keywords = {Center for Artificial Intelligence, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Visual analytics, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed}, pages = {121--132}, } @article{zeppelzauer_establishing_2015, title = {Establishing the fundamentals for an elephant early warning and monitoring system}, volume = {8:409}, url = {http://www.biomedcentral.com/content/pdf/s13104-015-1370-y.pdf}, doi = {10/gb3pth}, abstract = {The decline of habitat for elephants due to expanding human activity is a serious conservation problem. This has continuously escalated the human–elephant conflict in Africa and Asia. Elephants make extensive use of powerful infrasonic calls (rumbles) that travel distances of up to several kilometers. This makes elephants well-suited for acoustic monitoring because it enables detecting elephants even if they are out of sight. In sight, their distinct visual appearance makes them a good candidate for visual monitoring. We provide an integrated overview of our interdisciplinary project that established the scientific fundamentals for a future early warning and monitoring system for humans who regularly experience serious conflict with elephants. We first draw the big picture of an early warning and monitoring system, then review the developed solutions for automatic acoustic and visual detection, discuss specific challenges and present open future work necessary to build a robust and reliable early warning and monitoring system that is able to operate in situ. We present a method for the automated detection of elephant rumbles that is robust to the diverse noise sources present in situ. We evaluated the method on an extensive set of audio data recorded under natural field conditions. Results show that the proposed method outperforms existing approaches and accurately detects elephant rumbles. Our visual detection method shows that tracking elephants in wildlife videos (of different sizes and postures) is feasible and particularly robust at near distances. From our project results we draw a number of conclusions that are discussed and summarized. We clearly identified the most critical challenges and necessary improvements of the proposed detection methods and conclude that our findings have the potential to form the basis for a future automated early warning system for elephants. We discuss challenges that need to be solved and summarize open topics in the context of a future early warning and monitoring system. We conclude that a long-term evaluation of the presented methods in situ using real-time prototypes is the most important next step to transfer the developed methods into practical implementation.}, journal = {BMC Research Notes}, author = {Zeppelzauer, Matthias and Stöger, A.}, month = sep, year = {2015}, keywords = {2015, Acoustic monitoring, Audio Analysis, Automatic call detection, Call classification, Center for Artificial Intelligence, Classification, Creative Industries, Department Medien und Digitale Technologien, Department Technologie, Elephants, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Loxodonta africana, Machine Learning, Media Computing Group, Noise Reduction, Object Detection, Pattern recognition, Publikationstyp Schriftpublikation, Signal Enhancement, Video Analysis, Visual monitoring, Visual tracking, Vocalizations, Wiss. Beitrag, best, human–elephant conflict, peer-reviewed}, pages = {15}, } @article{wieser_study_2016, title = {A {Study} on {Skeletonization} of {Complex} {Petroglyph} {Shapes}}, issn = {1573-7721}, url = {http://link.springer.com/article/10.1007/s11042-016-3395-1}, doi = {10/ghpp2r}, abstract = {In this paper, we present a study on skeletonization of real-world shape data. The data stem from the cultural heritage domain and represent contact tracings of prehistoric petroglyphs. Automated analysis can support the work of archeologists on the investigation and categorization of petroglyphs. One strategy to describe petroglyph shapes is skeleton-based. The skeletonization of petroglyphs is challenging since their shapes are complex, contain numerous holes and are often incomplete or disconnected. Thus they pose an interesting testbed for skeletonization. We present a large real-world dataset consisting of more than 1100 petroglyph shapes. We investigate their properties and requirements for the purpose of skeletonization, and evaluate the applicability of state-of-the-art skeletonization and skeleton pruning algorithms on this type of data. Experiments show that pre-processing of the shapes is crucial to obtain robust skeletons. We propose an adaptive pre-processing method for petroglyph shapes and improve several state-of-the-art skeletonization algorithms to make them suitable for the complex material. Evaluations on our dataset show that 79.8 \% of all shapes can be improved by the proposed pre-processing techniques and are thus better suited for subsequent skeletonization. Furthermore we observe that a thinning of the shapes produces robust skeletons for 83.5 \% of our shapes and outperforms more sophisticated skeletonization techniques.}, journal = {Multimedia Tools and Applications (Springer)}, author = {Wieser, Ewald and Seidl, Markus and Zeppelzauer, Matthias}, year = {2016}, note = {Projekt: PITOTI 3D}, keywords = {2016, Center for Artificial Intelligence, Computer Vision, Department Medien und Digitale Technologien, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Pattern recognition, Publikationstyp Schriftpublikation, Real-world shape data, Shape pre-processing, Skeletionization, Skeletonization, Wiss. Beitrag, best, peer-reviewed, petroglyphs}, pages = {1--19}, } @article{zeppelzauer_multimodal_2016, title = {Multimodal classification of events in social media}, issn = {0262-8856}, url = {https://arxiv.org/pdf/1601.00599}, doi = {10/ghpp2q}, abstract = {Abstract A large amount of social media hosted on platforms like Flickr and Instagram is related to social events. The task of social event classification refers to the distinction of event and non-event-related contents as well as the classification of event types (e.g. sports events and concerts). In this paper, we provide an extensive study of textual, visual, as well as multimodal representations for social event classification. We investigate the strengths and weaknesses of the modalities and study the synergy effects between the modalities. Experimental results obtained with our multimodal representation outperform state-of-the-art methods and provide a new baseline for future research.}, journal = {Image and Vision Computing}, author = {Zeppelzauer, Matthias and Schopfhauser, Daniel}, year = {2016}, keywords = {2016, Center for Artificial Intelligence, Computer Vision, Department Medien und Digitale Technologien, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Multimodal retrieval, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed}, } @article{seidl_gradual_2011, title = {Gradual transition detection in historic film material —a systematic study}, volume = {4}, issn = {1556-4673}, url = {http://doi.acm.org/10.1145/2069276.2069279}, doi = {10/fzsqr8}, number = {3}, journal = {J. Comput. Cult. Herit.}, author = {Seidl, Markus and Zeppelzauer, Matthias and Mitrović, Dalibor and Breiteneder, Christian}, year = {2011}, keywords = {Center for Artificial Intelligence, Computer Vision, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, best-lbseidl, peer-reviewed}, pages = {10:1--10:18}, } @article{slijepcevic_explainable_2023, title = {Explainable {Machine} {Learning} in {Human} {Gait} {Analysis}: {A} {Study} on {Children} {With} {Cerebral} {Palsy}}, volume = {11}, copyright = {CC-BY-NC-ND}, issn = {2169-3536}, shorttitle = {Explainable {Machine} {Learning} in {Human} {Gait} {Analysis}}, url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10164110}, doi = {10.1109/ACCESS.2023.3289986}, abstract = {This work investigates the effectiveness of various machine learning (ML) methods in classifying human gait patterns associated with cerebral palsy (CP) and examines the clinical relevance of the learned features using explainability approaches. We trained different ML models, including convolutional neural networks, self-normalizing neural networks, random forests, and decision trees, and generated explanations for the trained models. For the deep neural networks, Grad-CAM explanations were aggregated on different levels to obtain explanations at the decision, class and model level. We investigate which subsets of 3D gait analysis data are particularly suitable for the classification of CP-related gait patterns. The results demonstrate the superiority of kinematic over ground reaction force data for this classification task and show that traditional ML approaches such as random forests and decision trees achieve better results and focus more on clinically relevant regions compared to deep neural networks. The best configuration, using sagittal knee and ankle angles with a random forest, achieved a classification accuracy of 93.4 \% over all four CP classes (crouch gait, apparent equinus, jump gait, and true equinus). Deep neural networks utilized not only clinically relevant features but also additional ones for their predictions, which may provide novel insights into the data and raise new research questions. Overall, the article provides insights into the application of ML in clinical practice and highlights the importance of explainability to promote trust and understanding of ML models.}, journal = {IEEE Access}, author = {Slijepcevic, Djordje and Zeppelzauer, Matthias and Unglaube, Fabian and Kranzl, Andreas and Breiteneder, Christian and Horsak, Brian}, year = {2023}, note = {Conference Name: IEEE Access}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health and Social Innovation, Departement Gesundheit, Department Gesundheit, Department Medien und Digitale Technologien, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, SP CDHSI Motor Rehabilitation, Wiss. Beitrag, best, best-bhorsak, peer-reviewed}, pages = {65906--65923}, } @article{horst_modeling_2023, title = {Modeling biological individuality using machine learning: {A} study on human gait}, volume = {21}, copyright = {CC-BY-NC-ND}, issn = {2001-0370}, shorttitle = {Modeling biological individuality using machine learning}, doi = {10.1016/j.csbj.2023.06.009}, abstract = {Human gait is a complex and unique biological process that can offer valuable insights into an individual's health and well-being. In this work, we leverage a machine learning-based approach to model individual gait signatures and identify factors contributing to inter-individual variability in gait patterns. We provide a comprehensive analysis of gait individuality by (1) demonstrating the uniqueness of gait signatures in a large-scale dataset and (2) highlighting the gait characteristics that are most distinctive to each individual. We utilized the data from three publicly available datasets comprising 5368 bilateral ground reaction force recordings during level overground walking from 671 distinct healthy individuals. Our results show that individuals can be identified with a prediction accuracy of 99.3\% by using the bilateral signals of all three ground reaction force components, with only 10 out of 1342 recordings in our test data being misclassified. This indicates that the combination of bilateral ground reaction force signals with all three components provides a more comprehensive and accurate representation of an individual's gait signature. The highest accuracy was achieved by (linear) Support Vector Machines (99.3\%), followed by Random Forests (98.7\%), Convolutional Neural Networks (95.8\%), and Decision Trees (82.8\%). The proposed approach provides a powerful tool to better understand biological individuality and has potential applications in personalized healthcare, clinical diagnosis, and therapeutic interventions.}, language = {eng}, journal = {Computational and Structural Biotechnology Journal}, author = {Horst, Fabian and Slijepcevic, Djordje and Simak, Marvin and Horsak, Brian and Schöllhorn, Wolfgang Immanuel and Zeppelzauer, Matthias}, year = {2023}, pmid = {37416082}, pmcid = {PMC10319823}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health and Social Innovation, Departement Gesundheit, Departement Medien und Digitale Technologien, Department Gesundheit, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Musculoskeletal Simulations, Phaidra, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Wiss. Beitrag, best, best-bhorsak, peer-reviewed}, pages = {3414--3423}, } @inproceedings{slijepcevic_explaining_2022, series = {{ESMAC} 2022 {Abstracts}}, title = {Explaining machine learning models for age classification in human gait analysis}, volume = {97}, url = {https://www.sciencedirect.com/science/article/pii/S0966636222003538}, doi = {10.1016/j.gaitpost.2022.07.153}, language = {en}, urldate = {2022-11-11}, booktitle = {Gait \& {Posture}}, author = {Slijepcevic, D. and Horst, F. and Simak, M. and Lapuschkin, S. and Raberger, A. M. and Samek, W. and Breiteneder, C. and Schöllhorn, W. I. and Zeppelzauer, M. and Horsak, B.}, month = sep, year = {2022}, note = {Projekt: I3D}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health and Social Innovation, Department Gesundheit, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Vortrag, Wiss. Beitrag, best, peer-reviewed}, pages = {S252--S253}, } @inproceedings{dumphart_automated_2021, series = {{ESMAC} 2021 {Abstracts}}, title = {An automated deep learning-based gait event detection algorithm for various pathologies}, volume = {90}, url = {https://www.sciencedirect.com/science/article/pii/S0966636221003350}, doi = {https://doi.org/10.1016/j.gaitpost.2021.09.026}, language = {en}, urldate = {2021-10-15}, booktitle = {Gait \& {Posture}}, author = {Dumphart, B. and Slijepčević, D. and Unglaube, F. and Kranzl, A. and Baca, A. and Zeppelzauer, M. and Horsak, B.}, month = oct, year = {2021}, note = {Projekt: ELSA}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, SP CDHSI Motor Rehabilitation, Vortrag, Wiss. Beitrag, best, peer-reviewed}, pages = {50--51}, } @inproceedings{krondorfer_deep_2021, series = {{ESMAC} 2021 {Abstracts}}, title = {Deep learning-based similarity retrieval in clinical {3D} gait analysis}, volume = {90}, url = {https://www.sciencedirect.com/science/article/pii/S0966636221003751}, doi = {https://doi.org/10.1016/j.gaitpost.2021.09.066}, language = {en}, urldate = {2021-10-15}, booktitle = {Gait \& {Posture}}, author = {Krondorfer, P. and Slijepčević, D. and Unglaube, F. and Kranzl, A. and Breiteneder, C. and Zeppelzauer, M. and Horsak, B.}, month = oct, year = {2021}, note = {Projekt: I3D}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, SP CDHSI Motor Rehabilitation, Vortrag, Wiss. Beitrag, best, peer-reviewed}, pages = {127--128}, } @inproceedings{nurgazina_visualization_2022, address = {Hagenberg, Austria}, title = {Visualization and clustering for rolling forecast quality verification: {A} case study in the automotive industry}, volume = {200}, copyright = {CC BY-NC-ND}, url = {https://www.sciencedirect.com/science/article/pii/S1877050922003131}, doi = {https://doi.org/10.1016/j.procs.2022.01.304}, language = {en}, booktitle = {Procedia {Computer} {Science}}, publisher = {Elsevier B.V.}, author = {Nurgazina, Jamilya and Felberbauer, Thomas and Asprion, Bernward and Pinnamaraju, Pavan}, year = {2022}, note = {Project: InnoFIT}, keywords = {Center for Artificial Intelligence, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Data Intelligence, Institut für Creative Media Technologies, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, Vortrag, Wiss. Beitrag, best, peer-reviewed, ⛔ No DOI found}, pages = {1048--1057}, } @inproceedings{kovac_classification_2022, address = {Barcelona, Spain}, title = {Classification of rain events using directional radio data of commercial microwave links}, isbn = {978-1-66548-356-8}, url = {https://ieeexplore.ieee.org/document/9855003/}, doi = {10.1109/COINS54846.2022.9855003}, urldate = {2022-11-23}, booktitle = {2022 {IEEE} {International} {Conference} on {Omni}-layer {Intelligent} {Systems} ({COINS})}, publisher = {IEEE}, author = {Kovac, Fabian and Eigner, Oliver and Adrowitzer, Alexander and Scholnast, Hubert and Buchelt, Alexander}, month = aug, year = {2022}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Data Intelligence, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, SP IT Sec System \& Application Security, Vortrag, Wiss. Beitrag, best, peer-reviewed}, pages = {1--6}, } @article{slijepcevic_kanonymity_2021, title = {k‑{Anonymity} in {Practice}: {How} {Generalisation} and {Suppression} {Affect} {Machine} {Learning} {Classifiers}}, volume = {111}, copyright = {Open Access}, issn = {0167-4048}, url = {https://doi.org/10.1016/j.cose.2021.102488}, doi = {10.1016/j.cose.2021.102488}, journal = {Computers \& Security}, author = {Slijepčević, Djordje and Henzl, Maximilian and Klausner, Lukas Daniel and Dam, Tobias and Kieseberg, Peter and Zeppelzauer, Matthias}, month = oct, year = {2021}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed}, pages = {19}, } @article{adensamer_computer_2021, title = {“{Computer} {Says} {No}”: {Algorithmic} {Decision} {Support} and {Organisational} {Responsibility}}, volume = {7–8}, copyright = {Open Access}, doi = {10/gm6t7q}, journal = {Journal of Responsible Technology}, author = {Adensamer, Angelika and Gsenger, Rita and Klausner, Lukas Daniel}, month = oct, year = {2021}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, Wiss. Beitrag, best, peer-reviewed}, } @article{adensamer_part_2021, title = {“{Part} {Man}, {Part} {Machine}, {All} {Cop}”: {Automation} in {Policing}}, volume = {2021}, copyright = {Open Access}, doi = {10/gk3q27}, number = {4}, journal = {Frontiers in Artificial Intelligence}, author = {Adensamer, Angelika and Klausner, Lukas Daniel}, month = jun, year = {2021}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, Wiss. Beitrag, best, peer-reviewed}, } @inproceedings{holzinger_digital_2021, address = {Cham}, title = {Digital {Transformation} for {Sustainable} {Development} {Goals} ({SDGs}) - {A} {Security}, {Safety} and {Privacy} {Perspective} on {AI}}, isbn = {978-3-030-84060-0}, abstract = {The main driver of the digital transformation currently underway is undoubtedly artificial intelligence (AI). The potential of AI to benefit humanity and its environment is undeniably enormous. AI can definitely help find new solutions to the most pressing challenges facing our human society in virtually all areas of life: from agriculture and forest ecosystems that affect our entire planet, to the health of every single human being. However, this article highlights a very different aspect. For all its benefits, the large-scale adoption of AI technologies also holds enormous and unimagined potential for new kinds of unforeseen threats. Therefore, all stakeholders, governments, policy makers, and industry, together with academia, must ensure that AI is developed with these potential threats in mind and that the safety, traceability, transparency, explainability, validity, and verifiability of AI applications in our everyday lives are ensured. It is the responsibility of all stakeholders to ensure the use of trustworthy and ethically reliable AI and to avoid the misuse of AI technologies. Achieving this will require a concerted effort to ensure that AI is always consistent with human values and includes a future that is safe in every way for all people on this planet. In this paper, we describe some of these threats and show that safety, security and explainability are indispensable cross-cutting issues and highlight this with two exemplary selected application areas: smart agriculture and smart health.}, booktitle = {Machine {Learning} and {Knowledge} {Extraction}}, publisher = {Springer International Publishing}, author = {Holzinger, Andreas and Weippl, Edgar and Tjoa, A. Min and Kieseberg, Peter}, editor = {Holzinger, Andreas and Kieseberg, Peter and Tjoa, A. Min and Weippl, Edgar}, year = {2021}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Data Intelligence, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, Vortrag, best, best lbkieseberg, peer-reviewed}, pages = {1--20}, } @inproceedings{zeppelzauer_novel_2015, address = {Boston, MA, USA}, title = {A {Novel} {Annotation} {Tool} for {Complex} {Petroglyph} {Shapes}}, abstract = {We present a novel semi-automatic annotation tool for the construction of large real-world shape datasets. The tool enables the collaborative semi-automatic segmentation and annotation of shapes. Shapes are stored together with their annotations in a database and can be retrieved efficiently to construct custom shape datasets. The resulting datasets should stimulte further reasearch in the domain of shape recognition and matching.}, booktitle = {The {Future} of {Datasets} in {Vision} {Workshop} (in conjunction with {CVPR} 2015)}, author = {Zeppelzauer, Matthias and Wieser, Ewald and Seidl, Markus}, year = {2015}, note = {Projekt: PITOTI 3D}, keywords = {2015, Center for Artificial Intelligence, Creative Industries, Department Medien und Digitale Technologien, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Publikationstyp Präsentation, Publikationstyp Schriftpublikation, Vortrag, Wiss. Beitrag, best, peer-reviewed, poster, ⛔ No DOI found}, } @book{fensel_proceedings_2018, address = {Amsterdam}, series = {Procedia {Computer} {Science}}, title = {Proceedings of the 14th {International} {Conference} on {Semantic} {Systems}}, volume = {137}, url = {https://www.sciencedirect.com/journal/procedia-computer-science/vol/137}, publisher = {Elsevier}, author = {Fensel, Anna and de Boer, Victor and Pellegrini, Tassilo and Kiesling, Elmar and Haslhofer, Bernhard and Hollink, Laura and Schindler, Alexander}, year = {2018}, keywords = {Center for Artificial Intelligence, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Medienwirtschaft, Open Access, Publikationstyp Schriftpublikation, best, peer-reviewed}, } @book{fensel_proceedings_2016, address = {New York}, series = {{ICPS}}, title = {Proceedings of the 12th {International} {Conference} on {Semantic} {Systems}}, isbn = {978-1-4503-4752-5}, url = {http://dl.acm.org/citation.cfm?id=2993318}, language = {en}, publisher = {ACM}, author = {Fensel, Anna and Zaveri, Amrapali and Hellmann, Sebastian and Pellegrini, Tassilo}, year = {2016}, keywords = {Center for Artificial Intelligence, Department Wirtschaft, Forschungsgruppe Digital Technologies, Institut für Medienwirtschaft, Publikationstyp Schriftpublikation, best}, } @incollection{krone_digital_2016, address = {Wiesbaden}, title = {Digital {Rights} {Management} - {Technologien}, {Anwendungsbereiche} und {Entwicklungsperspektiven}}, isbn = {978-3-658-09632-8}, url = {http://link.springer.com/10.1007/978-3-658-09632-8_79-1}, language = {de}, urldate = {2016-12-04}, booktitle = {Handbuch {Medienökonomie}}, publisher = {Springer Fachmedien Wiesbaden}, author = {Pellegrini, Tassilo}, editor = {Krone, Jan and Pellegrini, Tassilo}, year = {2016}, keywords = {Center for Artificial Intelligence, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Creative Media Technologies, Institut für Medienwirtschaft, Institute for Innovation Systems, Publikationstyp Schriftpublikation, best, peer-reviewed}, pages = {1--19}, } @incollection{pellegrini_netzneutralitat_2017, address = {Wiesbaden}, title = {Netzneutralität und digitaler {Medienvertrieb}}, volume = {1}, url = {https://doi.org/10.1007/978-3-658-09632-8_17-1;}, language = {de}, booktitle = {Handbuch {Medienökonomie}. {Life} {Edition}}, publisher = {Springer Fachmedien}, author = {Pellegrini, Tassilo and Krone, Jan}, year = {2017}, keywords = {Center for Artificial Intelligence, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Creative Media Technologies, Institut für Medienwirtschaft, Institute for Innovation Systems, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, peer-reviewed}, pages = {347--368}, } @incollection{kovarova-simecek_reporting_2017, address = {Wiesbaden, Deutschland}, title = {Reporting to {Machines} - reporting trends an {XBRL} diffusion among {Austrian} listed companies.}, url = {https://www.springer.com/de/book/9783658172534}, booktitle = {Conference {Proceedings} {Trends} in {Business} {Communication} 2016}, publisher = {Springer Gabler}, author = {Kovarova-Simecek, Monika and Pellegrini, Tassilo}, editor = {Becker, T. and Schreckleitner, W. and Reitberger, W. and Brunner-Sperdin, A.}, year = {2017}, keywords = {BlogFincom, Center for Artificial Intelligence, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Medienwirtschaft, Publikationstyp Schriftpublikation, best}, pages = {49--66}, } @inproceedings{blumenstein_bringing_2017, title = {Bringing {Your} {Own} {Device} into {Multi}-device {Ecologies} - {A} {Technical} {Concept}}, url = {http://mc.fhstp.ac.at/sites/default/files/publications/1040_Blumenstein.pdf}, doi = {10/ghppx8}, abstract = {Almost every visitor brings their own mobile device (e.g., smartphone or tablet) to the museum. Although, many museums include interactive exhibits (e.g., multi-touch tables), the visitors’ own devices are rarely used as part of a device ecology. Currently, there is no suitable infrastructure to seamlessly link different devices in museums. Our approach is to integrate the visitor’s own device in a multi-device ecology (MDE) in the museum to enhance the visitor’s exhibition experience. Thus, we present a technical concept to set up such MDEs integrating the well-established TUIO framework for multi-touch interaction on and between devices.}, booktitle = {Proceedings of the 2017 {ACM} {International} {Conference} on {Interactive} {Surfaces} and {Spaces}}, publisher = {ACM}, author = {Blumenstein, Kerstin and Kaltenbrunner, Martin and Seidl, Markus and Breban, Laura and Thür, Niklas and Aigner, Wolfgang}, month = oct, year = {2017}, note = {Projekt: MEETeUX}, keywords = {Center for Artificial Intelligence, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Poster, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, best-kblumenstein, best-lbseidl, peer-reviewed}, pages = {306--311}, } @inproceedings{zielinski_persistence_2019, address = {Macao, China}, title = {Persistence {Bag}-of-{Words} for {Topological} {Data} {Analysis}}, url = {http://arxiv.org/abs/1802.04852}, doi = {10/ghpp7z}, urldate = {2018-10-10}, booktitle = {Proceedings of the {International} {Joint} {Conference} on {Artificial} {Intelligence} 2019}, author = {Zielinski, Bartosz and {Lipinski, Michal} and Juda, Mateusz and Zeppelzauer, Matthias and {Dlotko, Pawel}}, year = {2019}, note = {arXiv: 1802.04852}, keywords = {Artificial Intelligence, Center for Artificial Intelligence, Computer Vision, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Mathematics - Algebraic Topology, Media Computing Group, Statistics, Vortrag, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed}, pages = {6}, } @article{zaharieva_cross-platform_2015, title = {Cross-{Platform} {Social} {Event} {Detection}}, volume = {22}, issn = {1070-986X}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7045414&tag=1}, doi = {10/gh3773}, abstract = {It is becoming more and more popular to share personal experiences on platforms such as Flickr and YouTube. Uploaded media is usually described by both technical and user-generated metadata that is commonly used for their access and retrieval. Thus, a crucial aspect in this context is the quality and reliability of provided metadata. The mining of media across sharing platforms bears the additional challenge about potential di⬚erences in the maintained metadata. In order to provide a baseline for further research, we perform a thorough evaluation of the usefulness of available metadata in the context of social event detection in both single media repository scenario and across di⬚erent platforms.}, number = {3}, journal = {IEEE Multimedia}, author = {Zaharieva, Maia and Del Fabro, Manfred and Zeppelzauer, Matthias}, month = jan, year = {2015}, keywords = {2015, Center for Artificial Intelligence, Computer Vision, Creative Industries, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Pattern recognition, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed}, pages = {14}, } @incollection{holzinger_explainable_2018, address = {Cham}, title = {Explainable {AI}: {The} {New} 42?}, volume = {11015}, isbn = {978-3-319-99739-1 978-3-319-99740-7}, shorttitle = {Explainable {AI}}, url = {http://link.springer.com/10.1007/978-3-319-99740-7_21}, abstract = {Explainable AI is not a new field. Since at least the early exploitation of C.S. Pierce’s abductive reasoning in expert systems of the 1980s, there were reasoning architectures to support an explanation function for complex AI systems, including applications in medical diagnosis, complex multi-component design, and reasoning about the real world. So explainability is at least as old as early AI, and a natural consequence of the design of AI systems. While early expert systems consisted of handcrafted knowledge bases that enabled reasoning over narrowly well-defined domains (e.g., INTERNIST, MYCIN), such systems had no learning capabilities and had only primitive uncertainty handling. But the evolution of formal reasoning architectures to incorporate principled probabilistic reasoning helped address the capture and use of uncertain knowledge.}, language = {en}, urldate = {2019-01-23}, booktitle = {Machine {Learning} and {Knowledge} {Extraction}}, publisher = {Springer International Publishing}, author = {Goebel, Randy and Chander, Ajay and Holzinger, Katharina and Lecue, Freddy and Akata, Zeynep and Stumpf, Simone and Kieseberg, Peter and Holzinger, Andreas}, editor = {Holzinger, Andreas and Kieseberg, Peter and Tjoa, A Min and Weippl, Edgar}, year = {2018}, doi = {10.1007/978-3-319-99740-7_21}, keywords = {Center for Artificial Intelligence, Center for Digital Health Innovation, FH SP Cyber Security, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, SP IT Sec Security Management \& Privacy, best, peer-reviewed}, pages = {295--303}, } @inproceedings{amiri_efficiently_2019, address = {Lissabon}, title = {Efficiently {Vectorized} {Anonymization} in {Data} {Mining} using {Genetic} {Algorithms}}, volume = {Proceedings of the 34th International Conference on ICT Systems Security and Privacy Protection - IFIP SEC 2019}, author = {Amiri, Fatemeh and Quirchmayr, Gerald and Kieseberg, Peter and Bertone, Alessio and Weippl, Edgar}, month = jun, year = {2019}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, Vortrag, best, peer-reviewed}, } @inproceedings{rind_trustworthy_2022, title = {Trustworthy {Visual} {Analytics} in {Clinical} {Gait} {Analysis}: {A} {Case} {Study} for {Patients} with {Cerebral} {Palsy}}, isbn = {978-1-66549-356-7}, url = {https://arxiv.org/abs/2208.05232}, doi = {10.1109/TREX57753.2022.00006}, abstract = {Three-dimensional clinical gait analysis is essential for selecting optimal treatment interventions for patients with cerebral palsy (CP), but generates a large amount of time series data. For the automated analysis of these data, machine learning approaches yield promising results. However, due to their black-box nature, such approaches are often mistrusted by clinicians. We propose gaitXplorer, a visual analytics approach for the classification of CP-related gait patterns that integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications. Regions of high relevance for classification are highlighted in the interactive visual interface. The approach is evaluated in a case study with two clinical gait experts. They inspected the explanations for a sample of eight patients using the visual interface and expressed which relevance scores they found trustworthy and which they found suspicious. Overall, the clinicians gave positive feedback on the approach as it allowed them a better understanding of which regions in the data were relevant for the classification.}, booktitle = {Proc. 2022 {IEEE} {Workshop} on {TRust} and {EXpertise} in {Visual} {Analytics} ({TREX})}, publisher = {IEEE}, author = {Rind, Alexander and Slijepcevic, Djordje and Zeppelzauer, Matthias and Unglaube, Fabian and Kranzl, Andreas and Horsak, Brian}, year = {2022}, note = {Projekt: SoniVis Projekt: ReMoCap-Lab Projekt: I3D}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Data Science, Departement Medien und Digitale Technologien, Department Gesundheit, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Human-Computer Interaction, Institut für Creative Media Technologies, Machine Learning, SP CDHSI Motor Rehabilitation, Schriftpublikation, Visual Computing, Visualization, Vortrag, Wiss. Beitrag, best, best-arind, peer-reviewed}, pages = {7--15}, } @article{slijepcevic_explaining_2022, title = {Explaining {Machine} {Learning} {Models} for {Clinical} {Gait} {Analysis}}, volume = {3}, copyright = {CC-BY-NC-SA}, issn = {2691-1957}, url = {https://doi.org/10.1145/3474121}, doi = {10.1145/3474121}, number = {2}, journal = {ACM Transactions on Computing for Healthcare}, author = {Slijepcevic, Djordje and Horst, Fabian and Lapuschkin, Sebastian and Horsak, Brian and Raberger, Anna-Maria and Kranzl, Andreas and Samek, Wojciech and Breitender, Christian and Schöllhorn, Wolfgang and Zeppelzauer, Matthias}, year = {2022}, note = {Projekt: I3D Projekt: ReMoCapLab Projekt: DHLab}, keywords = {2020, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, Department Gesundheit, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, Studiengang Physiotherapie, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed}, pages = {14:1--14:27}, } @inproceedings{zeppelzauer_automatic_2018, address = {Yokohama, Japan}, title = {Automatic {Prediction} of {Building} {Age} from {Photographs}}, isbn = {978-1-4503-5046-4}, url = {https://arxiv.org/pdf/1804.02205}, doi = {10/ghpp2k}, language = {en}, urldate = {2018-10-10}, booktitle = {Proceedings of the {ACM} {International} {Conference} on {Multimedia} {Retrieval} ({ICMR} '18)}, publisher = {ACM Press}, author = {Zeppelzauer, Matthias and Despotovic, Miroslav and Sakeena, Muntaha and Koch, David and Döller, Mario}, year = {2018}, note = {Projekt: ImmBild Projekt: ImmoAge}, keywords = {Center for Artificial Intelligence, Computer Vision, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Publikationstyp Schriftpublikation, Visual Computing, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed}, pages = {126--134}, } @misc{kovac_keynote_2023, address = {Prague, Czech Republic}, title = {[{Keynote}] {Standing} {Still} {Is} {Not} {An} {Option}: {Alternative} {Baselines} for {Attainable} {Utility} {Preservation}}, shorttitle = {Standing {Still} {Is} {Not} {An} {Option}}, url = {https://mlprague.com/}, abstract = {The rapid development of machine learning and artificial intelligence in general has led to growing concerns about the potential impact of AI on society. Ensuring that AI systems behave safely and beneficially is a major challenge, particularly in the context of Reinforcement Learning, where an agent learns by interacting with an environment and receiving feedback in the form of rewards. Avoiding negative side-effects is one of those challenges, where the agent should not cause unintended harm while trying to achieve its primary objective. A promising way to accomplish this task in an implicit way without telling the agent what not to do, is Attainable Utility Preservation (AUP). AUP is a safe Reinforcement Learning approach that minimizes side-effects by optimizing for a primary reward function while preserving the ability to optimize auxiliary reward functions. However, AUP's applicability is limited to tasks where a no-op action (e.g., standing sill) is available in the agent's action space. Depending on the environment, this cannot always be guaranteed. To overcome this limitation, we introduce new baselines for AUP, which are applicable to environments with or without a no-op action in the agent's action space. We achieve this by regularizing the primary reward function in different ways with respect to auxiliary goals, depending on the used variation. This enables designers of environments to define simple reward functions, which then get extended by our introduced baselines to induce safer behavior. We evaluate all introduced variants on multiple AI safety gridworlds, which were specifically designed to test the agent's ability to solve a primary objective while avoiding negative side-effects. These effects include e.g., facing the agent in front of several options where only one solution without a side-effect is imminent, refraining from causing damage or interfering with the environment's dynamics, rescuing items without destroying them, or to learn how to mitigate delayed effects to some extent and to not complete the primary objective on purpose. We show how our approach induces safe, conservative, and effective behavior, even when a no-op action is not available for the agent. An additional benefit lies in the variation-based approach, which allows to consider multiple variants depending on the tasks to solve. In conclusion, our work addresses critical challenges in AI safety related to Reinforcement Learning and proposes an updated approach to achieve safe behavior implicitly by avoiding negative side-effects, contributing to the broader effort of designing safe and beneficial AI systems for the future.}, language = {en}, author = {Kovac, Fabian}, month = jun, year = {2023}, keywords = {Center for Artificial Intelligence, Computer Science, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Reinforcement Learning, Data Science, Depart Informatik und Security, Forschungsgruppe Data Intelligence, Institut für IT Sicherheitsforschung, Reinforcement Learning, SP IT Sec Applied Security \& Data Science, Vortrag, best}, } @article{kieseberg_secure_2021, title = {Secure {Internal} {Data} {Markets}}, volume = {13}, copyright = {Open Access}, url = {https://www.mdpi.com/1999-5903/13/8/208/pdf}, doi = {https://doi.org/10.3390/fi13080208}, abstract = {The data market concept has gained a lot of momentum in recent years, fuelled by initiatives to set up such markets, eg, on the European level. Still, the typical data market concept aims at providing a centralised platform with all of its positive and negative side effects. Internal data markets, also called local or on-premise data markets, on the other hand, are set up to allow data trade inside an institution (eg, between divisions of a large company) or between members of a small, well-defined consortium, thus allowing the remuneration of providing data inside these structures. Still, while research on securing global data markets has garnered some attention throughout recent years, the internal data markets have been treated as being more or less similar in this respect. In this paper, we outline the major differences between global and internal data markets with respect to security and why further research is required. Furthermore, we provide a fundamental model for a secure internal data market that can be used as a starting point for the generation of concrete internal data market models. Finally, we provide an overview on the research questions we deem most pressing in order to make the internal data market concept work securely, thus allowing for more widespread adoption.}, number = {8}, journal = {Future Internet}, author = {Kieseberg, Peter and Schrittwieser, Sebastian and Weippl, Edgar}, month = aug, year = {2021}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Data Intelligence, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, SP IT Sec Applied Security \& Data Science, best, best lbkieseberg, peer-reviewed}, } @article{zielinski_persistence_2021, title = {Persistence {Codebooks} for {Topological} {Data} {Analysis}}, volume = {54}, copyright = {Open Access}, issn = {0269-2821}, url = {https://rdcu.be/b6ENZ}, doi = {https://doi.org/10.1007/s10462-020-09897-4}, journal = {Journal of Artificial Intelligence Review}, author = {Zielinski, Bartosz and Lipinski, Michal and Juda, Mateusz and {Zeppelzauer, Matthias} and {Dlotko, Pawel}}, year = {2021}, keywords = {3D surface classification, Center for Artificial Intelligence, Computer Vision, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Green OA, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Open Access, Surface texture analysis, Visual Computing, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed, persistence diagram, persistence image, persistent homology, surface representation, surface topology analysis}, pages = {1969--2009}, } @inproceedings{seidl_markus_towards_2019, address = {Dublin, Irland}, title = {Towards {Distinction} of {Rock} {Art} {Pecking} {Styles} with a {Hybrid} {2D}/{3D} {Approach}}, booktitle = {Proceedings of the {International} {Conference} on {Content}-based {Multimedia} {Indexing} ({CBMI})}, author = {{Seidl, Markus} and {Zeppelzauer, Matthias}}, year = {2019}, note = {Projekt: PITOTI 3D}, keywords = {Center for Artificial Intelligence, Computer Vision, Digital Heritage, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Visual Computing, Vortrag, Wiss. Beitrag, best, peer-reviewed, ⛔ No DOI found}, pages = {4}, } @inproceedings{pellegrini_integrating_2012, address = {Graz, Österreich}, series = {{ICPS}}, title = {Integrating {Linked} {Data} into the {Content} {Value} {Chain}}, isbn = {978-1-4503-1112-0}, abstract = {Only a few media companies have so far (as of April 2012) publicly declared engagement in the area of Linked Data. Nevertheless among the chosen few are BBC Online, the New York Times, The Guardian and Reuters who utilize Linked Data to add significant value to the news production process. This paper discusses achievements and challenges in utilizing semantic metadata in the news production process. It illustrates how Linked Data can be integrated into the content value chain and provide added value to content-related workflows without necessarily disrupting them. This is insofar critical as publishing companies react very sensitively to radical changes in their working settings and are often very suspicious of technologically induced innovations. Given the fact that Linked Data is a highly technology driven phenomenon that incrementally incorporates the culture, speech and logic of the engineering discipline, it is necessary to translate the benefits of Linked Data into the thinking and understanding of the publishing sector by illustrating the intersections between the traditional editorial content value chain and Linked Data as a complementary resource to innovate existing products and services.}, language = {en}, publisher = {ACM}, author = {Pellegrini, Tassilo}, year = {2012}, keywords = {Center for Artificial Intelligence, Department Wirtschaft, Forschungsgruppe Media Business, Institut für Creative Media Technologies, Institut für Medienwirtschaft, Institute for Innovation Systems, Publikationstyp Schriftpublikation, best, peer-reviewed}, pages = {8}, } @inproceedings{pellegrini_semantic_2011, address = {Baden-Baden}, title = {Semantic {Web} in netzbasierten {Unterhaltungsmedien}. {Bausteine} für eine {Metadaten}-Ökonomik}, isbn = {978-3-8329-6876-2}, url = {http://www.amazon.de/%C3%96konomie-Qualit%C3%A4t-Management-von-Unterhaltungsmedien/dp/3832968768/ref=sr_1_1?s=books&ie=UTF8&qid=1322133153&sr=1-1}, language = {de}, booktitle = {Ökonomie, {Qualität} und {Management} von {Unterhaltungsmedien}. {Tagungsband} der deutsche {Gesellschaft} für {Publizistik} und {Kommunikationswissenschaft}}, publisher = {Nomos}, author = {Pellegrini, Tassilo}, year = {2011}, keywords = {Center for Artificial Intelligence, Department Wirtschaft, Institut für Medienwirtschaft, Institute for Innovation Systems, Publikationstyp Schriftpublikation, best, peer-reviewed}, pages = {253--276}, } @inproceedings{slobodanka_sesame_2010, address = {New York}, series = {{ICPS}}, title = {{SESAME} demonstrator: ontologies, services and policies for energy efficiency}, isbn = {978-1-4503-0014-8}, url = {http://dl.acm.org/citation.cfm?id=1839707.1839738&coll=DL&dl=GUIDE&CFID=70037251&CFTOKEN=18476634}, doi = {10/d2zbnm}, language = {en}, publisher = {ACM}, author = {Slobodanka, Tomic and Fensel, Anna and Pellegrini, Tassilo}, year = {2010}, keywords = {Center for Artificial Intelligence, Department Wirtschaft, Institut für Medienwirtschaft, Institute for Innovation Systems, Publikationstyp Schriftpublikation, best, peer-reviewed}, } @inproceedings{schmiedl_usage_2009, address = {Bonn Germany}, title = {Usage and {Usability} of the {Mobile} {Web}}, url = {http://doi.acm.org/10.1145/1613858.1613942}, doi = {10/fnv3pk}, abstract = {Browsing the Web on mobile phones has finally hit the mass. The visualization of websites on latest mobile phone models comes close to what we are used from desktop computers. Tailoring websites for mobile phones seems to be not mandatory anymore. But still the small display size limits the user experience when browsing the web on these devices. As a result although access to the full web is reasonably well working a tendency to providing additional versions of mobile optimized versions of websites can be observed. This paper presents a multidimensional study where usage scenarios as well as the usability of mobile tailored compared to full websites were investigated. The results show clearly that users prefer and effectively do benefit from mobile optimized versions. However content providers sometimes do not understand the mobile scenarios in which their sites are used and consequently begin optimizing the functionality at the wrong end.}, booktitle = {Proceedings of the 11th {International} {Conference} on {Human}-{Computer} {Interaction} with {Mobile} {Devices} and {Services} - {MobileHCI} ’09}, publisher = {ACM}, author = {Schmiedl, Grischa and Seidl, Markus and Temper, Klaus}, year = {2009}, keywords = {2009, Center for Artificial Intelligence, Department Technologie, Extern, Institut für Creative Media Technologies, Institut für Medieninformatik, Mobile Phones, Mobile Web, Publikationstyp Schriftpublikation, best, usability}, pages = {2}, } @inproceedings{kieseberg_structural_2018, address = {Ljubljana, Slovenia}, title = {Structural {Limitations} of {B}+-{Tree} forensics}, isbn = {978-1-4503-6515-4}, url = {http://dl.acm.org/citation.cfm?doid=3277570.3277579}, doi = {10/gh372c}, abstract = {Despite the importance of databases in virtually all data driven applications, database forensics is still not the thriving topic it ought to be. Many database management systems (DBMSs) structure the data in the form of trees, most notably B+-Trees. Since the tree structure is depending on the characteristics of the INSERT-order, it can be used in order to generate information on later manipulations, as was shown in a previously published approach.}, language = {en}, urldate = {2019-01-23}, booktitle = {Proceedings of the {Central} {European} {Cybersecurity} {Conference} 2018 on - {CECC} 2018}, publisher = {ACM Press}, author = {Kieseberg, Peter and Schrittwieser, Sebastian and Weippl, Edgar}, year = {2018}, keywords = {Center for Artificial Intelligence, FH SP Cyber Security, Forschungsgruppe Secure Societies, Institut für IT Sicherheitsforschung, Vortrag, best, peer-reviewed}, pages = {1--4}, } @inproceedings{seidl_automated_2012, address = {New York, NY, USA}, series = {{ICVGIP} '12}, title = {Automated petroglyph image segmentation with interactive classifier fusion}, isbn = {978-1-4503-1660-6}, url = {http://doi.acm.org/10.1145/2425333.2425399}, doi = {10/gh372j}, booktitle = {Proceedings of the {Eighth} {Indian} {Conference} on {Computer} {Vision}, {Graphics} and {Image} {Processing}}, publisher = {ACM}, author = {Seidl, Markus and Breiteneder, Christian}, year = {2012}, note = {Projekt: FORSCH08 Projekt: PITOTI 3D}, keywords = {Center for Artificial Intelligence, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Pattern recognition, Publikationstyp Präsentation, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, best-lbseidl, experimental study, image features, image segmentation, peer-reviewed, petroglyphs, pixel classification, rock art}, pages = {66:1--66:8}, }