Publications

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Abkar, M., Zehtabiyan-Rezaie, N. & Iosifidis, A. (2023). Reinforcement learning for wind-farm flow control: Current state and future actions. Theoretical and Applied Mechanics Letters, 13(6), Artikel 100475. https://doi.org/10.1016/j.taml.2023.100475

Amarloo, A., Cinnella, P., Iosifidis, A., Forooghi, P. & Abkar, M. (2023). Data-driven Reynolds stress models based on the frozen treatment of Reynolds stress tensor and Reynolds force vector. Physics of Fluids, 35(7), Artikel 075154. https://doi.org/10.1063/5.0160977

Amarloo, A., Cinnella, P., Iosifidis, A., Forooghi, P. & Abkar, M. (2023). Investigation of Data-driven algebraic Reynolds stress models for turbulent secondary flows. I The 14th International ERCOFTAC Symposium on Engineering Turbulence Modelling and Measurements Proceedings https://www.ercoftac.org/etmm/program/conference-program/

Angelov, P., Bernardi, M. L., Nardini, F. M., Pecori, R., Valerio, L., Dini, P., Ashraf, S., Aversano, L., Bianchini, M., Brutti, A., Bukovsky, I., Cagnoni, S., Cao, J., Cimitile, M., Dazzi, P., Iammarino, M., Iosifidis, A., Michahelles, F., Mora, A. ... Welsh, M. (2024). PerconAI 2024: 3rd Workshop on Pervasive and Resource-Constrained Artificial Intelligence - Welcome and Committees. I 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (s. 34-35). IEEE. https://doi.org/10.1109/PerComWorkshops59983.2024.10503466

Ärje, J., Kärkkäinen, S., Meissner, K., Iosifidis, A., Ince, T., Gabbouj, M. & Kiranyaz, S. (2017). The effect of automated taxa identification errors on biological indices. Expert Systems with Applications, 72, 108-120. https://doi.org/10.1016/j.eswa.2016.12.015

Ärje, J., Milioris, D., Tran, D. T., Jepsen, J. U., Raitoharju, J., Gabbouj, M., Iosifidis, A. & Høye, T. T. (2019). Automatic Flower Detection and Classification System Uing a Light-Weight Convolutional Neural Network. I 27th European Signal Processing Conference EUSIPCO 2019 IEEE. http://eusipco2019.org/Proceedings/papertitles.html

Ärje, J., Raitoharju, J., Iosifidis, A., Tirronen, V., Meissner, K., Gabbouj, M., Kiranyaz, S. & Kärkkäinen, S. (2020). Human experts vs. machines in taxa recognition. Signal Processing: Image Communication, 87, Artikel 115917. https://doi.org/10.1016/j.image.2020.115917

Ärje, J., Melvad, C., Jeppesen, M. R., Madsen, S. A., Raitoharju, J., Rasmussen, M. S., Iosifidis, A., Tirronen, V., Gabbouj, M., Meissner, K. & Høye, T. T. (2020). Automatic image-based identification and biomass estimation of invertebrates. Methods in Ecology and Evolution, 11(8), 922-931. https://doi.org/10.1111/2041-210X.13428

Aytekin, C., Iosifidis, A., Kiranyaz, S. & Gabbouj, M. (2017). Learning Graph Affinities for Spectral Graph-based Salient Object Detection. Pattern Recognition, 64, 159-167. https://doi.org/10.1016/j.patcog.2016.11.005

Aytekin, C., Kiranyaz, S., Iosifidis, A. & Gabbouj, M. (2016). Recent advances in salient object detection -- Towards object recognition in big media data. Futura – Big Data, 35(2), 80-92. https://tutcris.tut.fi/portal/en/publications/recent-advances-in-salient-object-detection--towards-object-recognition-in-big-media-data(1e2959cf-8622-4243-b78b-6eb03f34a0ce).html

Aytekin, C., Iosifidis, A. & Gabbouj, M. (2018). Probabilistic Saliency Estimation. Pattern Recognition, 74, 359-372. https://doi.org/10.1016/j.patcog.2017.09.023

Aytekin, C., Iosifidis, A., Kiranyaz, S. & Gabbouj, M. (2016). Salient Object Segmentation based on Linearly Combined Affinity Graphs. I 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (s. 3769-3774). Artikel 7900221 IEEE. https://doi.org/10.1109/ICPR.2016.7900221

Bajovic, D., Bakhtiarnia, A., Bravos, G., Brutti, A., Burkhardt, F., Cauchi, D., Chazapis, A., Cianco, C., Dall'Asen, N., Delic, V., Dimou, C., Djokic, D., Escobar-Molero, A., Esterle, L., Eyben, F., Farella, E., Festi, T., Geromitsos, A., Giakoumakis, G. ... Zammit, J. (2021). MARVEL: Multimodal Extreme Scale Data Analytics for Smart Cities Environments. I 2021 International Balkan Conference on Communications and Networking, BalkanCom 2021 (s. 143-147). IEEE. https://doi.org/10.1109/BalkanCom53780.2021.9593258

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2021). Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning. I 2021 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533875

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2021). Multi-Exit Vision Transformer for Dynamic Inference. I The 32nd British Machine Vision Conference https://www.semanticscholar.org/paper/Multi-Exit-Vision-Transformer-for-Dynamic-Inference-Bakhtiarnia-Zhang/26f61039857423c0aad2c44f0e7769cbb61246c9

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2022). Single-layer vision transformers for more accurate early exits with less overhead. Neural Networks, 153, 461-473. https://doi.org/10.1016/j.neunet.2022.06.038

Bakhtiarnia, A., Leporowski, B., Esterle, L. & Iosifidis, A. (2022). Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications. I 2022 IEEE International Smart Cities Conference (ISC2) IEEE. https://doi.org/10.1109/ISC255366.2022.9921939

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2023). Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training. I 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (s. 559-564). IEEE. https://doi.org/10.1109/SSCI52147.2023.10371805

Bakhtiarnia, A., Milošević, N., Zhang, Q., Bajović, D. & Iosifidis, A. (2023). Dynamic Split Computing for Efficient Deep Edge Intelligence. I ICASSP 2023: 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IEEE. https://doi.org/10.1109/ICASSP49357.2023.10096914

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2023). PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks. I 2023 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN54540.2023.10191962

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2024). Efficient High-Resolution Deep Learning: A Survey. ACM Computing Surveys, 56(7), 1 - 35. Artikel 181. https://doi.org/10.1145/3645107

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2024). ACCURATE GIGAPIXEL CROWD COUNTING BY ITERATIVE ZOOMING AND REFINEMENT. I ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (s. 6510-6514). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP48485.2024.10446065

Baltakys, K., Baltakienė, M., Heidari, N., Iosifidis, A. & Kanniainen, J. (2023). Predicting the trading behavior of socially connected investors: Graph neural network approach with implications to market surveillance. Expert Systems with Applications, 228, Artikel 120285. https://doi.org/10.1016/j.eswa.2023.120285

Böttjer, T., Ørnskov Rønsch, G., Gonçalves Gomes, C. Â., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2021). Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds. I A.-L. Andersen, R. Andersen, D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems: Proceedings of the Changeable, Agile, Reconﬁgurable and Virtual Production Conference and the World Mass Customization & Personalization Conference (s. 431-439). Springer. https://www.springerprofessional.de/en/data-driven-identification-of-remaining-useful-life-for-plastic-/19816878

Böttjer, T., Ørnskov Rønsch, G., Gomes, C., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2022). Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds. I A.-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021 (s. 431-439). Springer. https://doi.org/10.1007/978-3-030-90700-6_49

Böttjer, T., Tola, D., Kakavandi, F., Wewer, C. R., Ramanujan, D., Gomes, C., Larsen, P. G. & Iosifidis, A. (2023). A review of unit level digital twin applications in the manufacturing industry. CIRP Journal of Manufacturing Science and Technology, 45, 162-189. https://doi.org/10.1016/j.cirpj.2023.06.011

Cao, G., Iosifidis, A., Chen, K. & Gabbouj, M. (2018). Generalized Multi-View Embedding for Visual Recognition and Cross-Modal Retrieval. IEEE Transactions on Cybernetics, 48(9), 2542-2555. Artikel 8026149. https://doi.org/10.1109/TCYB.2017.2742705

Cao, G., Iosifidis, A. & Gabbouj, M. (2017). Multi-view Nonparametric Discriminant Analysis for Image Retrieval and Recognition. IEEE Signal Processing Letters, 24(10), 1537-1541. Artikel 8023967. https://doi.org/10.1109/LSP.2017.2748392

Cao, G., Iosifidis, A. & Gabbouj, M. (2018). Neural class-specific regression for face verification. IET Biometrics, 7(1), 63-70. https://doi.org/10.1049/iet-bmt.2017.0081

Cao, G., Waris, M. A., Iosifidis, A. & Gabbouj, M. (2017). Multi-modal Subspace Learning with Dropout regularization for Cross-modal Recognition and Retrieval. I 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016 Artikel 7821032 IEEE. https://doi.org/10.1109/IPTA.2016.7821032

Cao, G., Iosifidis, A., Chen, K. & Gabbouj, M. (2016). Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval. arXiv, 1-14 (+ 3). https://arxiv.org/pdf/1605.09696.pdf

Cao, G., Iosifidis, A., Gabbouj, M., Raghavan, V. & Gottumukkala, R. (2021). Deep Multi-view Learning to Rank. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1426-1438. Artikel 8845659. https://doi.org/10.1109/TKDE.2019.2942590

Cavagna, A., Li, N., Iosifidis, A. & Zhang, Q. (2023). Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications. I 2023 IEEE International Conference on Communications Workshops (ICC Workshops) (s. 1617-1622). IEEE. https://doi.org/10.1109/ICCWorkshops57953.2023.10283786

Chumachenko, K., Mannisto, A., Iosifidis, A. & Raitoharju, J. (2020). Machine Learning Based Analysis of Finnish World War II Photographers. IEEE Access, 8, 144184-144196. Artikel 9159591. https://doi.org/10.1109/ACCESS.2020.3014458

Chumachenko, K., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Speed-up and Multi-view Extensions to Subclass Discriminant Analysis. Pattern Recognition, 111, Artikel 107660. https://doi.org/10.1016/j.patcog.2020.107660

Chumachenko, K., Iosifidis, A. & Gabbouj, M. (2021). Robust fast subclass discriminant analysis. I 2020 28th European Signal Processing Conference (EUSIPCO) (s. 1397-1401). Artikel 9287557 IEEE. https://doi.org/10.23919/Eusipco47968.2020.9287557

Chumachenko, K., Raitoharju, J., Gabbouj, M. & Iosifidis, A. (2020). Incremental Fast Subclass Discriminant Analysis. I 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings (s. 1771-1775). Artikel 9191328 IEEE. https://doi.org/10.1109/ICIP40778.2020.9191328

Chumachenko, K., Iosifidis, A. & Gabbouj, M. (2022). Feedforward Neural Networks Initialization based on Discriminant Learning. Neural Networks, 146, 220-229. https://doi.org/10.1016/j.neunet.2021.11.020

Chumachenko, K., Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Rethinking compactness in deep neural networks. Afhandling præsenteret på International Conference on Machine Learning Workshop on Overparameterization. https://drive.google.com/file/d/1pu5xJMPvKuGFeVCcylEvLz00uLK3UN9I/view

Chumachenko, K., Gabbouj, M. & Iosifidis, A. (2022). Object detection and tracking. I A. Iosifidis & A. Tefas (red.), Deep Learning for Robot Perception and Cognition (s. 243-278). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00016-6

Dong, Z., Li, N., Iosifidis, A. & Zhang, Q. (2022). Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing. ArXiv. https://doi.org/10.48550/arXiv.2211.13778

Feng, H., Gomes, C., Thule, C., Lausdahl, K., Iosifidis, A. & Larsen, P. G. (2021). Introduction to Digital Twin Engineering. I C. R. Martin, M. J. Blas & A. I. Psijas (red.), 2021 Annual Modeling and Simulation Conference (ANNSIM) (s. 1-12). IEEE. https://doi.org/10.23919/ANNSIM52504.2021.9552135

Feng, H., Gomes, C., Iosifidis, A. & Larsen, P. G. (2021). Model-based monitoring and state estimation for digital twins: The Kalman filter. I C. R. Martin, M. J. Blas & A. I. Psijas (red.), Annual Modeling and Simulation Conference (ANNSIM 2021) (s. 1-2)

Frasheri, M., Katsaros, P., Iosifidis, A., Hansen, S. T., Gomes, C., Evans, V. T. & Larsen, P. G. (2024). System Monitoring through a Digital Twin. I J. Fitzgerald, C. Gomes & P. G. Larsen (red.), The Engineering of Digital Twins (s. 189-207). Springer. https://doi.org/10.1007/978-3-031-66719-0_9

Gautam, C., Tiwari, A. & Iosifidis, A. (2019). Minimum Variance-Embedded Multi-layer Kernel Ridge Regression for One-class Classification. I S. Sundaram (red.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (s. 389-396). Artikel 8628692 IEEE. https://doi.org/10.1109/SSCI.2018.8628692

Gautam, C., Tiwari, A., Mishra, P. K., Suresh, S., Iosifidis, A. & Tanveer, M. (2021). Graph-Embedded Multi-Layer Kernel Ridge Regression for One-Class Classification. Cognitive Computation, 13(2), 552-569. https://doi.org/10.1007/s12559-020-09804-7

Gomes, C., Lucani Rötter, D. E., Iosifidis, A., Feng, H., Ejersbo, H. & Frasheri, M. (2024). Sensing and Communication of Data from the Physical Twin. I J. Fitzgerald, C. Gomes & P. G. Larsen (red.), The Engineering of Digital Twins (s. 147-171). Springer. https://doi.org/10.1007/978-3-031-66719-0_7#citeas

Hansen, O. L. P., Svenning, J.-C., Olsen, K., Dupont, S., Garner, B. H., Iosifidis, A., Price, B. W. & Høye, T. T. (2019). Image data used for publication "Species-level image classification with convolutional neural network enable insect identification from habitus images". Datasæt https://doi.org/10.5281/zenodo.3549369

Hansen, O. L. P., Svenning, J. C., Olsen, K., Dupont, S., Garner, B. H., Iosifidis, A., Price, B. W. & Høye, T. T. (2020). Species-level image classification with convolutional neural network enables insect identification from habitus images. Ecology and Evolution, 10(2), 737-747. https://doi.org/10.1002/ece3.5921

Hedegaard, L. & Iosifidis, A. (2023). Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch. I L. Karlinsky, T. Michaeli & K. Nishino (red.), Computer Vision – ECCV 2022 Workshops, Proceedings (s. 21-34). Springer. https://doi.org/10.1007/978-3-031-25082-8_2

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Revideret 10.08.2023

Alexandros Iosifidis