Publications

Journal Articles

2025

Context-agnostic data visitation to exchange health data for scientific research: the example of PADME PHT

context-agnostic data visitation personal data exchange health research EU data protection law General Data Protection Regulation (GDPR) data sharing personal data processing health data data protection compliance individuals’ rights European Union law privacy governance research data access legal reconciliation data flow for research

Dara Hallinan , Marc Nestor , Muhammad Hamza Akhtar , Constantin Breß , Mehrshad Jaberansary , Christoph Lange , Macedo Maia , Fruzsina Molnár-Gábor , Yeliz Üçer Yediel , Zeyd Boukhers

International Data Privacy Law 2025

PDF DOI
Hallinan, D., Nestor, M., Akhtar, M. H., Breß, C., Jaberansary, M., Lange, C., Maia, M., Molnár-Gábor, F., Yediel, Y. Ü., & Boukhers, Z. (2025). Context-agnostic data visitation to exchange health data for scientific research: the example of PADME PHT. International Data Privacy Law. https://doi.org/10.1093/idpl/ipaf011

Hallinan, Dara, , and Marc Nestor, , and Muhammad Hamza Akhtar, , and Constantin Breß, , and Mehrshad Jaberansary, , and Christoph Lange, , and Macedo Maia, , and Fruzsina Molnár-Gábor, , and Yeliz Üçer Yediel, , and Zeyd Boukhers. "Context-agnostic data visitation to exchange health data for scientific research: the example of PADME PHT." International Data Privacy Law, 2025.

Hallinan, Dara, , and Marc Nestor, , and Muhammad Hamza Akhtar, , and Constantin Breß, , and Mehrshad Jaberansary, , and Christoph Lange, , and Macedo Maia, , and Fruzsina Molnár-Gábor, , and Yeliz Üçer Yediel, , and Zeyd Boukhers. "Context-agnostic data visitation to exchange health data for scientific research: the example of PADME PHT." International Data Privacy Law (2025).
@article{article11, title = {Context-agnostic data visitation to exchange health data for scientific research: the example of PADME PHT}, author = {Dara Hallinan and Marc Nestor and Muhammad Hamza Akhtar and Constantin Breß and Mehrshad Jaberansary and Christoph Lange and Macedo Maia and Fruzsina Molnár-Gábor and Yeliz Üçer Yediel and Zeyd Boukhers}, journal = {International Data Privacy Law}, year = {2025}, doi = {10.1093/idpl/ipaf011}, url = {https://doi.org/10.1093/idpl/ipaf011}, publisher = {Oxford University Press}, }
R4-SLAM: Toward Real-time, Robust, and Resource-Restricted Visual SLAM in Dynamic Environments

simultaneous localization and mapping (SLAM) visual SLAM dynamic environments semantic SLAM dynamic object mitigation edge devices real-time processing R4-SLAM semantic keyframe selection semantic front-end decoupling computational efficiency tracking stability processing speed accuracy evaluation TUM RGB-D dataset mobile robotics autonomous vehicles augmented reality

Zhijie Zhang , Cong Yang , Xue Qin , Wei Sui , Zeyd Boukhers , Hui Kong , Jie Liu , Zhijun Li

IEEE Transactions on Instrumentation and Measurement 2025
Zhang, Z., Yang, C., Qin, X., Sui, W., Boukhers, Z., Kong, H., Liu, J., & Li, Z. (2025). R4-SLAM: Toward Real-time, Robust, and Resource-Restricted Visual SLAM in Dynamic Environments. IEEE Transactions on Instrumentation and Measurement.

Zhang, Zhijie, , and Cong Yang, , and Xue Qin, , and Wei Sui, , and Zeyd Boukhers, , and Hui Kong, , and Jie Liu, , and Zhijun Li. "R4-SLAM: Toward Real-time, Robust, and Resource-Restricted Visual SLAM in Dynamic Environments." IEEE Transactions on Instrumentation and Measurement, 2025.

Zhang, Zhijie, , and Cong Yang, , and Xue Qin, , and Wei Sui, , and Zeyd Boukhers, , and Hui Kong, , and Jie Liu, , and Zhijun Li. "R4-SLAM: Toward Real-time, Robust, and Resource-Restricted Visual SLAM in Dynamic Environments." IEEE Transactions on Instrumentation and Measurement (2025).
@article{article12, title = {R4-SLAM: Toward Real-time, Robust, and Resource-Restricted Visual SLAM in Dynamic Environments}, author = {Zhijie Zhang and Cong Yang and Xue Qin and Wei Sui and Zeyd Boukhers and Hui Kong and Jie Liu and Zhijun Li}, journal = {IEEE Transactions on Instrumentation and Measurement}, year = {2025}, publisher = {IEEE}, }

2024

Skeleton ground truth extraction: methodology, annotation tool and benchmarks

skeleton ground truth deep learning heuristic extraction diagnosticity hypothesis SkeView tool evaluation baseline

Cong Yang , Bipin Indurkhya , John See , Bo Gao , Yan Ke , Zeyd Boukhers , Zhenyu Yang , Marcin Grzegorzek

International Journal of Computer Vision 132 1219-1241 2024

PDF DOI
Yang, C., Indurkhya, B., See, J., Gao, B., Ke, Y., Boukhers, Z., Yang, Z., & Grzegorzek, M. (2023). Skeleton ground truth extraction: methodology, annotation tool and benchmarks. International Journal of Computer Vision, 132(4), 1219–1241. https://doi.org/10.1007/s11263-023-01926-3

Yang, Cong, , and Bipin Indurkhya, , and John See, , and Bo Gao, , and Yan Ke, , and Zeyd Boukhers, , and Zhenyu Yang, , and Marcin Grzegorzek. "Skeleton ground truth extraction: methodology, annotation tool and benchmarks." International Journal of Computer Vision, vol. 132, no. 4, 2024, pp. 1219-1241.

Yang, Cong, , and Bipin Indurkhya, , and John See, , and Bo Gao, , and Yan Ke, , and Zeyd Boukhers, , and Zhenyu Yang, , and Marcin Grzegorzek. "Skeleton ground truth extraction: methodology, annotation tool and benchmarks." International Journal of Computer Vision 132, no. 4 (2024): 1219-1241.
@article{article1, title = {Skeleton ground truth extraction: methodology, annotation tool and benchmarks}, author = {Cong Yang and Bipin Indurkhya and John See and Bo Gao and Yan Ke and Zeyd Boukhers and Zhenyu Yang and Marcin Grzegorzek}, journal = {International Journal of Computer Vision}, volume = {132}, number = {4}, pages = {1219-1241}, year = {2024}, doi = {10.1007/s11263-023-01926-3}, url = {https://link.springer.com/content/pdf/10.1007/s11263-023-01926-3.pdf}, publisher = {Springer Science and Business Media LLC}, }
Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with the popularity of deep learning techniques. Furthermore, we see skeleton GTs used not only for training skeleton detectors with Convolutional Neural Networks (CNN), but also for evaluating skeleton-related pruning and matching algorithms. However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards. As a result, it is difficult to evaluate and reproduce CNN-based skeleton detectors and algorithms on a fair basis. In this paper, we present a heuristic strategy for object skeleton GT extraction in binary shapes and natural images. Our strategy is built on an extended theory of diagnosticity hypothesis, which enables encoding human-in-the-loop GT extraction based on clues from the target’s context, simplicity, and completeness. Using this strategy, we developed a tool, SkeView, to generate skeleton GT of 17 existing shape and image datasets. The GTs are then structurally evaluated with representative methods to build viable baselines for fair comparisons. Experiments demonstrate that GTs generated by our strategy yield promising quality with respect to standard consistency, and also provide a balance between simplicity and completeness.
BladeView: Towards Automatic Wind Turbine Inspection with Unmanned Aerial Vehicle

Wind turbines Blades Inspection Drones Autonomous aerial vehicles Automation Usability Trajectory Parameter estimation Erosion Wind turbine blade inspection drone inspection unmanned aerial vehicle wind power

Cong Yang , Hua Zhou , Xun Liu , Yan Ke , Bo Gao , Marcin Grzegorzek , Zeyd Boukhers , Tao Chen , John See

IEEE Transactions on Automation Science and Engineering 22 7530-7545 2024

PDF DOI
Yang, C., Zhou, H., Liu, X., Ke, Y., Gao, B., Grzegorzek, M., Boukhers, Z., Chen, T., & See, J. (2024). BladeView: Towards Automatic Wind Turbine Inspection with Unmanned Aerial Vehicle. IEEE Transactions on Automation Science and Engineering, 22, 7530–7545. https://doi.org/10.1109/tase.2024.3464640

Yang, Cong, , and Hua Zhou, , and Xun Liu, , and Yan Ke, , and Bo Gao, , and Marcin Grzegorzek, , and Zeyd Boukhers, , and Tao Chen, , and John See. "BladeView: Towards Automatic Wind Turbine Inspection with Unmanned Aerial Vehicle." IEEE Transactions on Automation Science and Engineering, vol. 22, 2024, pp. 7530-7545.

Yang, Cong, , and Hua Zhou, , and Xun Liu, , and Yan Ke, , and Bo Gao, , and Marcin Grzegorzek, , and Zeyd Boukhers, , and Tao Chen, , and John See. "BladeView: Towards Automatic Wind Turbine Inspection with Unmanned Aerial Vehicle." IEEE Transactions on Automation Science and Engineering 22 (2024): 7530-7545.
@article{article9, title = {BladeView: Towards Automatic Wind Turbine Inspection with Unmanned Aerial Vehicle}, author = {Cong Yang and Hua Zhou and Xun Liu and Yan Ke and Bo Gao and Marcin Grzegorzek and Zeyd Boukhers and Tao Chen and John See}, journal = {IEEE Transactions on Automation Science and Engineering}, volume = {22}, pages = {7530-7545}, year = {2024}, doi = {10.1109/tase.2024.3464640}, url = {https://doi.org/10.1109/tase.2024.3464640}, publisher = {IEEE}, }
This paper presents a fully automatic method, BladeView, for drone-based wind turbine blade inspection using an Unmanned Aerial Vehicle (UAV). With the need for highly efficient blade inspection coupled with the rapid increase of wind turbines, existing methods provide limited automation on wind turbine parameter estimation, full blade coverage, and safety control. We introduce an Automatic Parameter Calculation (APC) algorithm and an Automatic Flight System (AFS) in BladeView to compute wind turbine parameters and inspection paths, respectively. Leveraging triangulation and linear fitting integration techniques, the APC automatically calculates the wind turbine parameters and estimates the relative angle and position between a drone and the turbine. Furthermore, with dynamic path finding and B-spline optimization, the AFS plans a path covering 3 blades within specified flight corridors, in compliance with the turbine parameters obtained from APC. Thus, the proposed BladeView can properly ensure an inspection’s automation, coverage, safety, and smoothness. The efficiency and usability of BladeView are validated through 100,000 flight simulations in the Gazebo simulation environment and 9,239 field runs at various wind farms, including offshore, near-shore, deserts, mountainous areas, farmlands, and suburbs.Note to Practitioners—The proposed BladeView is distinguished in three aspects: (1) It automatically adapts wind turbines with varying geometric properties and physical locations relative to the take-off point. (2) It dramatically improves the quality of collected data with optimal UAV speed and flight corridors. (3) It thoroughly covers all three blades of a Horizontal-Axis Wind Turbine (HAWT), including regions where defects frequently occur. Thus, BladeView is more efficient and robust than existing UAV-based methods for blade inspection, with only around 25 minutes per HAWT. Moreover, it does not require experienced pilots to fly the UAV and manual interventions are rarely needed. Extensive simulation and real-world experiments demonstrate the efficiency and usability of BladeView in various on- and offshore wind farms.
A non-enhanced CT based deep learning diagnostic system for high-risk COVID-19 infection in lung cancer patients

COVID-19 non-enhanced CT deep learning lung cancer diagnostic system KL-6

Tianming Du , Yihao Sun , Xinghao Wang , Tao Jiang , Ning Xu , Zeyd Boukhers , Marcin Grzegorzek , Hongzan Sun , Chen Li

Frontiers in Medicine 11 1444708 2024

PDF DOI
Du, T., Sun, Y., Wang, X., Jiang, T., Xu, N., Boukhers, Z., Grzegorzek, M., Sun, H., & Li, C. (2024). A non-enhanced CT based deep learning diagnostic system for high-risk COVID-19 infection in lung cancer patients. Frontiers in Medicine, 11, 1444708. https://doi.org/10.3389/fmed.2024.1444708

Du, Tianming, , and Yihao Sun, , and Xinghao Wang, , and Tao Jiang, , and Ning Xu, , and Zeyd Boukhers, , and Marcin Grzegorzek, , and Hongzan Sun, , and Chen Li. "A non-enhanced CT based deep learning diagnostic system for high-risk COVID-19 infection in lung cancer patients." Frontiers in Medicine, vol. 11, 2024, pp. 1444708.

Du, Tianming, , and Yihao Sun, , and Xinghao Wang, , and Tao Jiang, , and Ning Xu, , and Zeyd Boukhers, , and Marcin Grzegorzek, , and Hongzan Sun, , and Chen Li. "A non-enhanced CT based deep learning diagnostic system for high-risk COVID-19 infection in lung cancer patients." Frontiers in Medicine 11 (2024): 1444708.
@article{article10, title = {A non-enhanced CT based deep learning diagnostic system for high-risk COVID-19 infection in lung cancer patients}, author = {Tianming Du and Yihao Sun and Xinghao Wang and Tao Jiang and Ning Xu and Zeyd Boukhers and Marcin Grzegorzek and Hongzan Sun and Chen Li}, journal = {Frontiers in Medicine}, volume = {11}, pages = {1444708}, year = {2024}, doi = {10.3389/fmed.2024.1444708}, url = {https://doi.org/10.3389/fmed.2024.1444708}, publisher = {Frontiers}, }
Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection. This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6. The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6. Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

2023

Knowledge guided multi-filter residual convolutional neural network for ICD coding from clinical text

ICD coding knowledge graph deep neural networks clinical text MIMIC-III KG-MultiResCNN

Zeyd Boukhers , Prantik Goswami , Jan Jürjens

Neural Computing and Applications 2023

PDF DOI
Boukhers, Z., Goswami, P., & Jürjens, J. (2023). Knowledge guided multi-filter residual convolutional neural network for ICD coding from clinical text. Neural Computing and Applications. https://doi.org/10.21203/rs.3.rs-1873701/v2

Boukhers, Zeyd, , and Prantik Goswami, , and Jan Jürjens. "Knowledge guided multi-filter residual convolutional neural network for ICD coding from clinical text." Neural Computing and Applications, 2023.

Boukhers, Zeyd, , and Prantik Goswami, , and Jan Jürjens. "Knowledge guided multi-filter residual convolutional neural network for ICD coding from clinical text." Neural Computing and Applications (2023).
@article{article2, title = {Knowledge guided multi-filter residual convolutional neural network for ICD coding from clinical text}, author = {Zeyd Boukhers and Prantik Goswami and Jan Jürjens}, journal = {Neural Computing and Applications}, year = {2023}, doi = {10.21203/rs.3.rs-1873701/v2}, url = {https://doi.org/10.21203/rs.3.rs-1873701/v2}, publisher = {Springer Nature}, }
One challenge often encountered when using Deep Neural Network models for automatic ICD coding is their potential inability to effectively handle unseen clinical texts, especially when these models are only trained on a limited number of examples. This is because these models rely solely on the patterns and relationships present in the training data, and may not be able to effectively incorporate additional knowledge about the relationships between medical entities. To address this issue, we introduce KG-MultiResCNN - Knowledge Guided Multi-filter Residual Convolutional Neural Network model, which combines training examples with external knowledge from the Wikidata Knowledge Graph (KG) in order to better capture the relationships between medical entities. The KG is a structured database that contains a wealth of information about various entities, including medical concepts and their relationships with one another. By incorporating this external knowledge into our model, we are able to improve its ability to predict ICD codes for new clinical texts. In our experiments with the MIMIC-III dataset, we found that the KG-MultiResCNN model significantly outperformed the baseline approaches. This demonstrates the effectiveness of using external knowledge, in addition to training examples, to improve the performance of deep learning models for automatic ICD coding.
Deep author name disambiguation using DBLP data

author name disambiguation DBLP co-author network neural network model bibliographic records digital libraries

Zeyd Boukhers , Nagaraj Bahubali Asundi

International Journal on Digital Libraries 25 431-441 2023

PDF DOI
Boukhers, Z., & Asundi, N. B. (2023). Deep author name disambiguation using DBLP data. International Journal on Digital Libraries, 25(3), 431–441. https://doi.org/10.1007/s00799-023-00361-6

Boukhers, Zeyd, , and Nagaraj Bahubali Asundi. "Deep author name disambiguation using DBLP data." International Journal on Digital Libraries, vol. 25, no. 3, 2023, pp. 431-441.

Boukhers, Zeyd, , and Nagaraj Bahubali Asundi. "Deep author name disambiguation using DBLP data." International Journal on Digital Libraries 25, no. 3 (2023): 431-441.
@article{article3, title = {Deep author name disambiguation using DBLP data}, author = {Zeyd Boukhers and Nagaraj Bahubali Asundi}, journal = {International Journal on Digital Libraries}, volume = {25}, number = {3}, pages = {431-441}, year = {2023}, doi = {10.1007/s00799-023-00361-6}, url = {https://doi.org/10.1007/s00799-023-00361-6}, publisher = {Springer Science and Business Media LLC}, }
In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.

2022

COIN: Counterfactual Image Generation for VQA Interpretation

counterfactual images visual question answering interpretability user study model behaviour

Zeyd Boukhers , Timo Hartmann , Jan Jürjens

Sensors 22 2245 2022

PDF DOI
Boukhers, Z., Hartmann, T., & Jürjens, J. (2022). COIN: Counterfactual Image Generation for VQA Interpretation. Sensors, 22(6), 2245. https://doi.org/10.3390/s22062245

Boukhers, Zeyd, , and Timo Hartmann, , and Jan Jürjens. "COIN: Counterfactual Image Generation for VQA Interpretation." Sensors, vol. 22, no. 6, 2022, pp. 2245.

Boukhers, Zeyd, , and Timo Hartmann, , and Jan Jürjens. "COIN: Counterfactual Image Generation for VQA Interpretation." Sensors 22, no. 6 (2022): 2245.
@article{article4, title = {COIN: Counterfactual Image Generation for VQA Interpretation}, author = {Zeyd Boukhers and Timo Hartmann and Jan Jürjens}, journal = {Sensors}, volume = {22}, number = {6}, pages = {2245}, year = {2022}, doi = {10.3390/s22062245}, url = {https://doi.org/10.3390/s22062245}, publisher = {MDPI}, }
Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced. However, they are still error-prone when dealing with relatively complex questions. Therefore, it is important to understand the behaviour of the VQA models before adopting their results. In this paper, we introduce an interpretability approach for VQA models by generating counterfactual images. Specifically, the generated image is supposed to have the minimal possible change to the original image and leads the VQA model to give a different answer. In addition, our approach ensures that the generated image is realistic. Since quantitative metrics cannot be employed to evaluate the interpretability of the model, we carried out a user study to assess different aspects of our approach. In addition to interpreting the result of VQA models on single images, the obtained results and the discussion provides an extensive explanation of VQA models’ behaviour.

2020

Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy

Topic modeling Renyi entropy Regularization Hyper-parameters Latent Dirichlet Allocation Hierarchical Dirichlet Process Additive Regularization of Topic Models

Sergei Koltsov , Vera Ignatenko , Zeyd Boukhers , Steffen Staab

Entropy 22 394 2020

PDF DOI
Koltsov, S., Ignatenko, V., Boukhers, Z., & Staab, S. (2020). Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy. Entropy, 22(4), 394. https://doi.org/10.3390/e22040394

Koltsov, Sergei, , and Vera Ignatenko, , and Zeyd Boukhers, , and Steffen Staab. "Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy." Entropy, vol. 22, no. 4, 2020, pp. 394.

Koltsov, Sergei, , and Vera Ignatenko, , and Zeyd Boukhers, , and Steffen Staab. "Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy." Entropy 22, no. 4 (2020): 394.
@article{article5, title = {Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy}, author = {Sergei Koltsov and Vera Ignatenko and Zeyd Boukhers and Steffen Staab}, journal = {Entropy}, volume = {22}, number = {4}, pages = {394}, year = {2020}, doi = {10.3390/e22040394}, url = {https://doi.org/10.3390/e22040394}, publisher = {MDPI}, }
Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models—Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)—we, first of all, show that the minimum of Renyi entropy coincides with the “true” number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.

2019

Fractal approach for determining the optimal number of topics in the field of topic modeling

Sergei Koltsov , Vera Ignatenko , Zeyd Boukhers , Steffen Staab

Journal of Physics: Conference Series 1163 012025 2019

PDF DOI
Koltsov, S., Ignatenko, V., Boukhers, Z., & Staab, S. (2019). Fractal approach for determining the optimal number of topics in the field of topic modeling. Journal of Physics: Conference Series, 1163, 012025. https://doi.org/10.1088/1742-6596/1163/1/012025

Koltsov, Sergei, , and Vera Ignatenko, , and Zeyd Boukhers, , and Steffen Staab. "Fractal approach for determining the optimal number of topics in the field of topic modeling." Journal of Physics: Conference Series, vol. 1163, 2019, pp. 012025.

Koltsov, Sergei, , and Vera Ignatenko, , and Zeyd Boukhers, , and Steffen Staab. "Fractal approach for determining the optimal number of topics in the field of topic modeling." Journal of Physics: Conference Series 1163 (2019): 012025.
@article{article6, title = {Fractal approach for determining the optimal number of topics in the field of topic modeling}, author = {Sergei Koltsov and Vera Ignatenko and Zeyd Boukhers and Steffen Staab}, journal = {Journal of Physics: Conference Series}, volume = {1163}, pages = {012025}, year = {2019}, doi = {10.1088/1742-6596/1163/1/012025}, url = {https://doi.org/10.1088/1742-6596/1163/1/012025}, publisher = {IOP Publishing}, }

2017

Example-based 3D trajectories of objects from 2D videos

Zeyd Boukhers , Kimiaki Shirahama , Marcin Grzegorzek

IEEE Transactions on Circuits and Systems for Video Technology 28 2246-2260 2017

PDF DOI
Boukhers, Z., Shirahama, K., & Grzegorzek, M. (2017). Example-based 3D trajectories of objects from 2D videos. IEEE Transactions on Circuits and Systems for Video Technology, 28(9), 2246–2260. https://doi.org/10.1109/tcsvt.2017.2727963

Boukhers, Zeyd, , and Kimiaki Shirahama, , and Marcin Grzegorzek. "Example-based 3D trajectories of objects from 2D videos." IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 9, 2017, pp. 2246-2260.

Boukhers, Zeyd, , and Kimiaki Shirahama, , and Marcin Grzegorzek. "Example-based 3D trajectories of objects from 2D videos." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 9 (2017): 2246-2260.
@article{article7, title = {Example-based 3D trajectories of objects from 2D videos}, author = {Zeyd Boukhers and Kimiaki Shirahama and Marcin Grzegorzek}, journal = {IEEE Transactions on Circuits and Systems for Video Technology}, volume = {28}, number = {9}, pages = {2246-2260}, year = {2017}, doi = {10.1109/tcsvt.2017.2727963}, url = {https://doi.org/10.1109/tcsvt.2017.2727963}, publisher = {IEEE}, }
Less Restrictive Camera Odometry from Monocular Camera

Zeyd Boukhers , Kimiaki Shirahama , Marcin Grzegorzek

Multimedia Tools and Applications 77 16199-16222 2017

PDF DOI
Boukhers, Z., Shirahama, K., & Grzegorzek, M. (2017). Less Restrictive Camera Odometry from Monocular Camera. Multimedia Tools and Applications, 77(13), 16199–16222. https://doi.org/10.1007/s11042-017-5195-7

Boukhers, Zeyd, , and Kimiaki Shirahama, , and Marcin Grzegorzek. "Less Restrictive Camera Odometry from Monocular Camera." Multimedia Tools and Applications, vol. 77, no. 13, 2017, pp. 16199-16222.

Boukhers, Zeyd, , and Kimiaki Shirahama, , and Marcin Grzegorzek. "Less Restrictive Camera Odometry from Monocular Camera." Multimedia Tools and Applications 77, no. 13 (2017): 16199-16222.
@article{article8, title = {Less Restrictive Camera Odometry from Monocular Camera}, author = {Zeyd Boukhers and Kimiaki Shirahama and Marcin Grzegorzek}, journal = {Multimedia Tools and Applications}, volume = {77}, number = {13}, pages = {16199-16222}, year = {2017}, doi = {10.1007/s11042-017-5195-7}, url = {https://doi.org/10.1007/s11042-017-5195-7}, publisher = {Springer International Publishing}, }

Conference Papers

2026

Unleashing Semantic and Geometric Priors for 3D Scene Completion

Shiyuan Chen , Wei Sui , Bohao Zhang , Zeyd Boukhers , John See , Cong Yang

Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence 2026
Chen, S., Sui, W., Zhang, B., Boukhers, Z., See, J., & Yang, C. (2026). Unleashing Semantic and Geometric Priors for 3D Scene Completion. Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence.

Chen, Shiyuan, , and Wei Sui, , and Bohao Zhang, , and Zeyd Boukhers, , and John See, , and Cong Yang. "Unleashing Semantic and Geometric Priors for 3D Scene Completion." Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence, 2026.

Chen, Shiyuan, , and Wei Sui, , and Bohao Zhang, , and Zeyd Boukhers, , and John See, , and Cong Yang. "Unleashing Semantic and Geometric Priors for 3D Scene Completion." Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence (2026).
@inproceedings{Chen2026unleashing, title = {Unleashing Semantic and Geometric Priors for 3D Scene Completion}, author = {Shiyuan Chen and Wei Sui and Bohao Zhang and Zeyd Boukhers and John See and Cong Yang}, booktitle = {Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence}, year = {2026}, }

2025

ELMTEX: Fine-tuning large language models for structured clinical information extraction. A case study on clinical reports

ELMTEX fine-tuning large language models structured clinical information extraction clinical reports

Aynur Guluzade , Naguib Heiba , Zeyd Boukhers , Florim Hamiti , Jahid Hasan Polash , Yehya Mohamad , Carlos A Velasco

International Conference on Artificial Intelligence in Medicine (AIME 2025) 181-185 2025

PDF DOI
Guluzade, A., Heiba, N., Boukhers, Z., Hamiti, F., Polash, J. H., Mohamad, Y., & Velasco, C. A. (2025). ELMTEX: Fine-tuning large language models for structured clinical information extraction. A case study on clinical reports. International Conference on Artificial Intelligence in Medicine (AIME 2025), 181–185. https://doi.org/10.1007/978-3-031-95841-0_34

Guluzade, Aynur, , and Naguib Heiba, , and Zeyd Boukhers, , and Florim Hamiti, , and Jahid Hasan Polash, , and Yehya Mohamad, , and Carlos A Velasco. "ELMTEX: Fine-tuning large language models for structured clinical information extraction. A case study on clinical reports." International Conference on Artificial Intelligence in Medicine (AIME 2025), 2025, pp. 181-185.

Guluzade, Aynur, , and Naguib Heiba, , and Zeyd Boukhers, , and Florim Hamiti, , and Jahid Hasan Polash, , and Yehya Mohamad, , and Carlos A Velasco. "ELMTEX: Fine-tuning large language models for structured clinical information extraction. A case study on clinical reports." International Conference on Artificial Intelligence in Medicine (AIME 2025) (2025): 181-185.
@inproceedings{Guluzade2025elmtex, title = {ELMTEX: Fine-tuning large language models for structured clinical information extraction. A case study on clinical reports}, author = {Aynur Guluzade and Naguib Heiba and Zeyd Boukhers and Florim Hamiti and Jahid Hasan Polash and Yehya Mohamad and Carlos A Velasco}, booktitle = {International Conference on Artificial Intelligence in Medicine (AIME 2025)}, pages = {181-185}, year = {2025}, doi = {10.1007/978-3-031-95841-0_34}, url = {https://doi.org/10.1007/978-3-031-95841-0_34}, publisher = {Springer Nature Switzerland}, }
Future-Proofing Heritage with ARGUS: A Multimodal Digital Twin Approach for Sustainable Preservation

George Pavlidis , Anestis Koutsoudis , Despoina Tsiafaki , Vasileios Sevetlidis , Vasileios Arampatzakis , Apostolos Sarris , Miltiadis Polidorou , Victor Klinkenberg , Zeyd Boukhers , Lingxiao Kong , Emeri Farinetti , Fernando Moreno-Navarro , Ioannis Kakogiannos , Sofia Aparicio , Javier Ortega , Fernando Ramonet , Athos Agapiou , Stylianos Hadjipetrou , Kyriakos Michaelides , Stavros Patsalidis , Phaedon Kyriakidis , Apostolos Papakonstantinou , Demetrios Athanasoulis , Christos Maris

International Conference on Computer Applications & Quantitative Methods in Archaeology 2025: "Digital Horizons" 2025

PDF DOI
Pavlidis, G., Koutsoudis, A., Tsiafaki, D., Sevetlidis, V., Arampatzakis, V., Sarris, A., Polidorou, M., Klinkenberg, V., Boukhers, Z., Kong, L., Farinetti, E., Moreno-Navarro, F., Kakogiannos, I., Aparicio, S., Ortega, J., Ramonet, F., Agapiou, A., Hadjipetrou, S., Michaelides, K., … Maris, C. (2025). Future-Proofing Heritage with ARGUS: A Multimodal Digital Twin Approach for Sustainable Preservation. International Conference on Computer Applications & Quantitative Methods in Archaeology 2025: "Digital Horizons". https://doi.org/10.24072/pci.archaeo.100624.d1

Pavlidis, George, , and Anestis Koutsoudis, , and Despoina Tsiafaki, , and Vasileios Sevetlidis, , and Vasileios Arampatzakis, , and Apostolos Sarris, , and Miltiadis Polidorou, , and Victor Klinkenberg, , and Zeyd Boukhers, , and Lingxiao Kong, , and Emeri Farinetti, , and Fernando Moreno-Navarro, , and Ioannis Kakogiannos, , and Sofia Aparicio, , and Javier Ortega, , and Fernando Ramonet, , and Athos Agapiou, , and Stylianos Hadjipetrou, , and Kyriakos Michaelides, , and Stavros Patsalidis, , and Phaedon Kyriakidis, , and Apostolos Papakonstantinou, , and Demetrios Athanasoulis, , and Christos Maris. "Future-Proofing Heritage with ARGUS: A Multimodal Digital Twin Approach for Sustainable Preservation." International Conference on Computer Applications & Quantitative Methods in Archaeology 2025: "Digital Horizons", 2025.

Pavlidis, George, , and Anestis Koutsoudis, , and Despoina Tsiafaki, , and Vasileios Sevetlidis, , and Vasileios Arampatzakis, , and Apostolos Sarris, , and Miltiadis Polidorou, , and Victor Klinkenberg, , and Zeyd Boukhers, , and Lingxiao Kong, , and Emeri Farinetti, , and Fernando Moreno-Navarro, , and Ioannis Kakogiannos, , and Sofia Aparicio, , and Javier Ortega, , and Fernando Ramonet, , and Athos Agapiou, , and Stylianos Hadjipetrou, , and Kyriakos Michaelides, , and Stavros Patsalidis, , and Phaedon Kyriakidis, , and Apostolos Papakonstantinou, , and Demetrios Athanasoulis, , and Christos Maris. "Future-Proofing Heritage with ARGUS: A Multimodal Digital Twin Approach for Sustainable Preservation." International Conference on Computer Applications & Quantitative Methods in Archaeology 2025: "Digital Horizons" (2025).
@inproceedings{Pavlidis2025future, title = {Future-Proofing Heritage with ARGUS: A Multimodal Digital Twin Approach for Sustainable Preservation}, author = {George Pavlidis and Anestis Koutsoudis and Despoina Tsiafaki and Vasileios Sevetlidis and Vasileios Arampatzakis and Apostolos Sarris and Miltiadis Polidorou and Victor Klinkenberg and Zeyd Boukhers and Lingxiao Kong and Emeri Farinetti and Fernando Moreno-Navarro and Ioannis Kakogiannos and Sofia Aparicio and Javier Ortega and Fernando Ramonet and Athos Agapiou and Stylianos Hadjipetrou and Kyriakos Michaelides and Stavros Patsalidis and Phaedon Kyriakidis and Apostolos Papakonstantinou and Demetrios Athanasoulis and Christos Maris}, booktitle = {International Conference on Computer Applications & Quantitative Methods in Archaeology 2025: "Digital Horizons"}, year = {2025}, doi = {10.24072/pci.archaeo.100624.d1}, url = {https://doi.org/10.24072/pci.archaeo.100624.d1}, publisher = {Peer Community In}, }
Q-Chain: A Causal-Aware Framework for Structural and Educational Question Generation

Junqi Xu , Lvcheng Wang , Zeyd Boukhers , Bipin Indurkhya , Cong Yang

The 15th ACM International Conference on Multimedia Retrieval 2083-2087 2025

PDF DOI
Xu, J., Wang, L., Boukhers, Z., Indurkhya, B., & Yang, C. (2025). Q-Chain: A Causal-Aware Framework for Structural and Educational Question Generation. The 15th ACM International Conference on Multimedia Retrieval, 2083–2087. https://doi.org/10.1145/3731715.3733492

Xu, Junqi, , and Lvcheng Wang, , and Zeyd Boukhers, , and Bipin Indurkhya, , and Cong Yang. "Q-Chain: A Causal-Aware Framework for Structural and Educational Question Generation." The 15th ACM International Conference on Multimedia Retrieval, 2025, pp. 2083-2087.

Xu, Junqi, , and Lvcheng Wang, , and Zeyd Boukhers, , and Bipin Indurkhya, , and Cong Yang. "Q-Chain: A Causal-Aware Framework for Structural and Educational Question Generation." The 15th ACM International Conference on Multimedia Retrieval (2025): 2083-2087.
@inproceedings{Xu2025q-chain, title = {Q-Chain: A Causal-Aware Framework for Structural and Educational Question Generation}, author = {Junqi Xu and Lvcheng Wang and Zeyd Boukhers and Bipin Indurkhya and Cong Yang}, booktitle = {The 15th ACM International Conference on Multimedia Retrieval}, pages = {2083-2087}, year = {2025}, doi = {10.1145/3731715.3733492}, url = {https://doi.org/10.1145/3731715.3733492}, publisher = {ACM}, }
A unified framework for cultural heritage data historicity and migration: The ARGUS approach

Lingxiao Kong , Apostolos Sarris , Miltiadis Polidorou , Victor Klingenberg , Vasilis Sevetlidis , Vasilis Arampatzakis , George Pavlidis , Cong Yang , Zeyd Boukhers

IEEE International Conference on Cyber Humanities 2025
Kong, L., Sarris, A., Polidorou, M., Klingenberg, V., Sevetlidis, V., Arampatzakis, V., Pavlidis, G., Yang, C., & Boukhers, Z. (2025). A unified framework for cultural heritage data historicity and migration: The ARGUS approach. IEEE International Conference on Cyber Humanities.

Kong, Lingxiao, , and Apostolos Sarris, , and Miltiadis Polidorou, , and Victor Klingenberg, , and Vasilis Sevetlidis, , and Vasilis Arampatzakis, , and George Pavlidis, , and Cong Yang, , and Zeyd Boukhers. "A unified framework for cultural heritage data historicity and migration: The ARGUS approach." IEEE International Conference on Cyber Humanities, 2025.

Kong, Lingxiao, , and Apostolos Sarris, , and Miltiadis Polidorou, , and Victor Klingenberg, , and Vasilis Sevetlidis, , and Vasilis Arampatzakis, , and George Pavlidis, , and Cong Yang, , and Zeyd Boukhers. "A unified framework for cultural heritage data historicity and migration: The ARGUS approach." IEEE International Conference on Cyber Humanities (2025).
@inproceedings{Kong2025a, title = {A unified framework for cultural heritage data historicity and migration: The ARGUS approach}, author = {Lingxiao Kong and Apostolos Sarris and Miltiadis Polidorou and Victor Klingenberg and Vasilis Sevetlidis and Vasilis Arampatzakis and George Pavlidis and Cong Yang and Zeyd Boukhers}, booktitle = {IEEE International Conference on Cyber Humanities}, year = {2025}, }
EMORL: Ensemble multi-objective reinforcement learning for efficient and flexible LLM fine-tuning

Lingxiao Kong , Cong Yang , Susanne Neufang , Oya Deniz Beyan , Zeyd Boukhers

Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue 417–430 2025

PDF
Kong, L., Yang, C., Neufang, S., Beyan, O. D., & Boukhers, Z. (2025). EMORL: Ensemble multi-objective reinforcement learning for efficient and flexible LLM fine-tuning. Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, 417–430. https://aclanthology.org/2025.sigdial-1.33/

Kong, Lingxiao, , and Cong Yang, , and Susanne Neufang, , and Oya Deniz Beyan, , and Zeyd Boukhers. "EMORL: Ensemble multi-objective reinforcement learning for efficient and flexible LLM fine-tuning." Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, 2025, pp. 417–430.

Kong, Lingxiao, , and Cong Yang, , and Susanne Neufang, , and Oya Deniz Beyan, , and Zeyd Boukhers. "EMORL: Ensemble multi-objective reinforcement learning for efficient and flexible LLM fine-tuning." Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue (2025): 417–430.
@inproceedings{Kong2025emorl, title = {EMORL: Ensemble multi-objective reinforcement learning for efficient and flexible LLM fine-tuning}, author = {Lingxiao Kong and Cong Yang and Susanne Neufang and Oya Deniz Beyan and Zeyd Boukhers}, booktitle = {Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue}, pages = {417–430}, year = {2025}, url = {https://aclanthology.org/2025.sigdial-1.33/}, publisher = {Association for Computational Linguistics}, }
Recent advances in reinforcement learning (RL) for large language model (LLM) fine-tuning show promise in addressing multi-objective tasks but still face significant challenges, including competing objective balancing, low training efficiency, poor scalability, and limited explainability. Leveraging ensemble learning principles, we introduce an Ensemble Multi-Objective RL (EMORL) framework that fine-tunes multiple models with individual objectives while optimizing their aggregation after the fine-tuning to improve efficiency and flexibility. Our method is the first to aggregate the hidden states of individual models, incorporating contextual information from multiple objectives. This approach is supported by a hierarchical grid search algorithm that identifies optimal weighted combinations. We evaluate EMORL on counselor reflection generation tasks, using text classification models to score the generations and provide rewards during RL fine-tuning. Through comprehensive experiments on the PAIR and Psych8k datasets, we demonstrate the advantages of EMORL against existing baselines: significantly lower and more stable training consumption (17,529 \ensuremath\pm 1,650 data points and 6,573 \ensuremath\pm 147.43 seconds), improved scalability and explainability, and comparable performance across multiple objectives.
Exploring the limits of model compression in LLMs: a knowledge distillation study on QA tasks

Joyeeta Datta , Niclas Doll , Qusai Ramadan , Zeyd Boukhers

Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue 477–483 2025

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Datta, J., Doll, N., Ramadan, Q., & Boukhers, Z. (2025). Exploring the limits of model compression in LLMs: a knowledge distillation study on QA tasks. Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, 477–483. https://aclanthology.org/2025.sigdial-1.39/

Datta, Joyeeta, , and Niclas Doll, , and Qusai Ramadan, , and Zeyd Boukhers. "Exploring the limits of model compression in LLMs: a knowledge distillation study on QA tasks." Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, 2025, pp. 477–483.

Datta, Joyeeta, , and Niclas Doll, , and Qusai Ramadan, , and Zeyd Boukhers. "Exploring the limits of model compression in LLMs: a knowledge distillation study on QA tasks." Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue (2025): 477–483.
@inproceedings{Datta2025exploring, title = {Exploring the limits of model compression in LLMs: a knowledge distillation study on QA tasks}, author = {Joyeeta Datta and Niclas Doll and Qusai Ramadan and Zeyd Boukhers}, booktitle = {Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue}, pages = {477–483}, year = {2025}, url = {https://aclanthology.org/2025.sigdial-1.39/}, publisher = {Association for Computational Linguistics}, }
Large Language Models (LLMs) have shown outstanding performance across a range of NLP tasks, but their computational demands hinder deployment in real-world, resource-constrained environments. This work investigates the extent to which LLMs can be compressed using knowledge distillation (KD) while maintaining strong performance on question answering (QA) tasks. We evaluate student models distilled from the Pythia and Qwen2.5 families on two QA benchmarks, SQuAD and MLQA, under zero-shot and one-shot prompting conditions. Results show that student models retain over 90% of their teacher models’ performance while reducing parameter counts by up to 57.1%. Furthermore, one-shot prompting yields additional performance gains over zero-shot setups for both model families. These findings underscore the trade-off between model efficiency and task performance, demonstrating that KD, combined with minimal prompting, can yield compact yet capable QA systems suitable for real-world applications.
FAIR Data Assessment Using LLMs: The Fair-Way

Anmol Sharma , Sulayman K. Sowe , Soo-Yon Kim , Sayed Hoseini , Fidan Limani , Zeyd Boukhers , Christoph Lange , Stefan Decker

The Conference on Information and Knowledge Management (CIKM) 5228-5232 2025

PDF DOI
Sharma, A., Sowe, S. K., Kim, S.-Y., Hoseini, S., Limani, F., Boukhers, Z., Lange, C., & Decker, S. (2025). FAIR Data Assessment Using LLMs: The Fair-Way. The Conference on Information and Knowledge Management (CIKM), 5228–5232. https://doi.org/10.1145/3746252.3760811

Sharma, Anmol, , and Sulayman K. Sowe, , and Soo-Yon Kim, , and Sayed Hoseini, , and Fidan Limani, , and Zeyd Boukhers, , and Christoph Lange, , and Stefan Decker. "FAIR Data Assessment Using LLMs: The Fair-Way." The Conference on Information and Knowledge Management (CIKM), 2025, pp. 5228-5232.

Sharma, Anmol, , and Sulayman K. Sowe, , and Soo-Yon Kim, , and Sayed Hoseini, , and Fidan Limani, , and Zeyd Boukhers, , and Christoph Lange, , and Stefan Decker. "FAIR Data Assessment Using LLMs: The Fair-Way." The Conference on Information and Knowledge Management (CIKM) (2025): 5228-5232.
@inproceedings{Sharma2025fair, title = {FAIR Data Assessment Using LLMs: The Fair-Way}, author = {Anmol Sharma and Sulayman K. Sowe and Soo-Yon Kim and Sayed Hoseini and Fidan Limani and Zeyd Boukhers and Christoph Lange and Stefan Decker}, booktitle = {The Conference on Information and Knowledge Management (CIKM)}, pages = {5228-5232}, year = {2025}, doi = {10.1145/3746252.3760811}, url = {https://doi.org/10.1145/3746252.3760811}, publisher = {ACM}, }

2024

YawnNet: A Visual-Centric Approach for Yawning Detection

Ruoxi Sun , Xinyu Yang , Cong Qian , Chenyu Zhu , Wei Sui , Zeyd Boukhers , Cong Yang

Proceedings of the 2024 International Conference on Multimedia Retrieval 1140–1144 2024

PDF DOI
Sun, R., Yang, X., Qian, C., Zhu, C., Sui, W., Boukhers, Z., & Yang, C. (2024). YawnNet: A Visual-Centric Approach for Yawning Detection. Proceedings of the 2024 International Conference on Multimedia Retrieval, 1140–1144. https://doi.org/10.1145/3652583.3657618

Sun, Ruoxi, , and Xinyu Yang, , and Cong Qian, , and Chenyu Zhu, , and Wei Sui, , and Zeyd Boukhers, , and Cong Yang. "YawnNet: A Visual-Centric Approach for Yawning Detection." Proceedings of the 2024 International Conference on Multimedia Retrieval, 2024, pp. 1140–1144.

Sun, Ruoxi, , and Xinyu Yang, , and Cong Qian, , and Chenyu Zhu, , and Wei Sui, , and Zeyd Boukhers, , and Cong Yang. "YawnNet: A Visual-Centric Approach for Yawning Detection." Proceedings of the 2024 International Conference on Multimedia Retrieval (2024): 1140–1144.
@inproceedings{Sun2024yawnet, title = {YawnNet: A Visual-Centric Approach for Yawning Detection}, author = {Ruoxi Sun and Xinyu Yang and Cong Qian and Chenyu Zhu and Wei Sui and Zeyd Boukhers and Cong Yang}, booktitle = {Proceedings of the 2024 International Conference on Multimedia Retrieval}, pages = {1140–1144}, year = {2024}, doi = {10.1145/3652583.3657618}, url = {https://doi.org/10.1145/3652583.3657618}, publisher = {ACM}, }
Moiré pattern detection: Stability and efficiency with evaluated loss function

Zhuocheng Li , Xizhu Shen , Simin Luan , Shuwei Guo , Zeyd Boukhers , Yuyi Wang , Cong Yang

International Conference on Pattern Recognition 1-16 2024

PDF DOI
Li, Z., Shen, X., Luan, S., Guo, S., Boukhers, Z., Wang, Y., & Yang, C. (2024). Moiré pattern detection: Stability and efficiency with evaluated loss function. International Conference on Pattern Recognition, 1–16. https://doi.org/10.1007/978-3-031-78444-6_3

Li, Zhuocheng, , and Xizhu Shen, , and Simin Luan, , and Shuwei Guo, , and Zeyd Boukhers, , and Yuyi Wang, , and Cong Yang. "Moiré pattern detection: Stability and efficiency with evaluated loss function." International Conference on Pattern Recognition, 2024, pp. 1-16.

Li, Zhuocheng, , and Xizhu Shen, , and Simin Luan, , and Shuwei Guo, , and Zeyd Boukhers, , and Yuyi Wang, , and Cong Yang. "Moiré pattern detection: Stability and efficiency with evaluated loss function." International Conference on Pattern Recognition (2024): 1-16.
@inproceedings{Li2024moire, title = {Moiré pattern detection: Stability and efficiency with evaluated loss function}, author = {Zhuocheng Li and Xizhu Shen and Simin Luan and Shuwei Guo and Zeyd Boukhers and Yuyi Wang and Cong Yang}, booktitle = {International Conference on Pattern Recognition}, pages = {1-16}, year = {2024}, doi = {10.1007/978-3-031-78444-6_3}, url = {https://doi.org/10.1007/978-3-031-78444-6_3}, publisher = {Springer Nature Switzerland}, }
Transition in focus of prediction tasks for skeleton graph component detection with Transformer

Transition Prediction tasks Skeleton graph Component detection Transformer

Zhiyuan Wang , Cong Yang , Yulu Zhang , Zeyd Boukhers , Wei Sui , Yi Ji , Chunping Liu

ACM Multimedia Asia 1-7 2024

PDF DOI
Wang, Z., Yang, C., Zhang, Y., Boukhers, Z., Sui, W., Ji, Y., & Liu, C. (2024). Transition in focus of prediction tasks for skeleton graph component detection with Transformer. ACM Multimedia Asia, 1–7. https://doi.org/10.1145/3696409.3700170

Wang, Zhiyuan, , and Cong Yang, , and Yulu Zhang, , and Zeyd Boukhers, , and Wei Sui, , and Yi Ji, , and Chunping Liu. "Transition in focus of prediction tasks for skeleton graph component detection with Transformer." ACM Multimedia Asia, 2024, pp. 1-7.

Wang, Zhiyuan, , and Cong Yang, , and Yulu Zhang, , and Zeyd Boukhers, , and Wei Sui, , and Yi Ji, , and Chunping Liu. "Transition in focus of prediction tasks for skeleton graph component detection with Transformer." ACM Multimedia Asia (2024): 1-7.
@inproceedings{Wang2024transition, title = {Transition in focus of prediction tasks for skeleton graph component detection with Transformer}, author = {Zhiyuan Wang and Cong Yang and Yulu Zhang and Zeyd Boukhers and Wei Sui and Yi Ji and Chunping Liu}, booktitle = {ACM Multimedia Asia}, pages = {1-7}, year = {2024}, doi = {10.1145/3696409.3700170}, url = {https://doi.org/10.1145/3696409.3700170}, publisher = {ACM}, }
Large language model in medical informatics: direct classification and enhanced text representations for automatic ICD coding

Zeyd Boukhers , AmeerAli Khan , Qusai Ramadan , Cong Yang

IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 1-4 2024

PDF DOI
Boukhers, Z., Khan, A. A., Ramadan, Q., & Yang, C. (2024). Large language model in medical informatics: direct classification and enhanced text representations for automatic ICD coding. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1–4. https://doi.org/10.1109/bibm62325.2024.10822419

Boukhers, Zeyd, , and AmeerAli Khan, , and Qusai Ramadan, , and Cong Yang. "Large language model in medical informatics: direct classification and enhanced text representations for automatic ICD coding." IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024, pp. 1-4.

Boukhers, Zeyd, , and AmeerAli Khan, , and Qusai Ramadan, , and Cong Yang. "Large language model in medical informatics: direct classification and enhanced text representations for automatic ICD coding." IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2024): 1-4.
@inproceedings{Boukhers2024large, title = {Large language model in medical informatics: direct classification and enhanced text representations for automatic ICD coding}, author = {Zeyd Boukhers and AmeerAli Khan and Qusai Ramadan and Cong Yang}, booktitle = {IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, pages = {1-4}, year = {2024}, doi = {10.1109/bibm62325.2024.10822419}, url = {https://doi.org/10.1109/bibm62325.2024.10822419}, publisher = {IEEE}, }
From Theory to Practice: Demonstrators of FAIR Data Spaces Across Different Sectors

Nikolaus Glombiewski , Zeyd Boukhers , Christian Beilschmidt , Johannes Drönner , Michael Mattig , Artur Piet , Robert Pietrzynski , Mehrshad Jaberansary , Macedo Maia , Sebastian Beyvers , Yeliz Üçer Yediel , Muhammad Hamza Akhtar , Heiner Oberkampf , Jonathan Hartman , Christoph Lange

The 40th ACM/SIGAPP Symposium On Applied Computing (SAC) 1-4 2024

PDF DOI
Glombiewski, N., Boukhers, Z., Beilschmidt, C., Drönner, J., Mattig, M., Piet, A., Pietrzynski, R., Jaberansary, M., Maia, M., Beyvers, S., Yediel, Y. Ü., Akhtar, M. H., Oberkampf, H., Hartman, J., & Lange, C. (2024). From Theory to Practice: Demonstrators of FAIR Data Spaces Across Different Sectors. The 40th ACM/SIGAPP Symposium On Applied Computing (SAC), 1–4. https://doi.org/10.1145/3672608.3707987

Glombiewski, Nikolaus, , and Zeyd Boukhers, , and Christian Beilschmidt, , and Johannes Drönner, , and Michael Mattig, , and Artur Piet, , and Robert Pietrzynski, , and Mehrshad Jaberansary, , and Macedo Maia, , and Sebastian Beyvers, , and Yeliz Üçer Yediel, , and Muhammad Hamza Akhtar, , and Heiner Oberkampf, , and Jonathan Hartman, , and Christoph Lange. "From Theory to Practice: Demonstrators of FAIR Data Spaces Across Different Sectors." The 40th ACM/SIGAPP Symposium On Applied Computing (SAC), 2024, pp. 1-4.

Glombiewski, Nikolaus, , and Zeyd Boukhers, , and Christian Beilschmidt, , and Johannes Drönner, , and Michael Mattig, , and Artur Piet, , and Robert Pietrzynski, , and Mehrshad Jaberansary, , and Macedo Maia, , and Sebastian Beyvers, , and Yeliz Üçer Yediel, , and Muhammad Hamza Akhtar, , and Heiner Oberkampf, , and Jonathan Hartman, , and Christoph Lange. "From Theory to Practice: Demonstrators of FAIR Data Spaces Across Different Sectors." The 40th ACM/SIGAPP Symposium On Applied Computing (SAC) (2024): 1-4.
@inproceedings{Glombiewski2024from, title = {From Theory to Practice: Demonstrators of FAIR Data Spaces Across Different Sectors}, author = {Nikolaus Glombiewski and Zeyd Boukhers and Christian Beilschmidt and Johannes Drönner and Michael Mattig and Artur Piet and Robert Pietrzynski and Mehrshad Jaberansary and Macedo Maia and Sebastian Beyvers and Yeliz Üçer Yediel and Muhammad Hamza Akhtar and Heiner Oberkampf and Jonathan Hartman and Christoph Lange}, booktitle = {The 40th ACM/SIGAPP Symposium On Applied Computing (SAC)}, pages = {1-4}, year = {2024}, doi = {10.1145/3672608.3707987}, url = {https://doi.org/10.1145/3672608.3707987}, publisher = {ACM}, }
FDO manager: minimum viable FAIR digital object implementation

FAIR Digital Objects data management machine-actionability research artefacts FDO Manager

Oussama Zoubia , Nagaraj Bahubali Asundi , Sezin Dogan , Adamantios Koumpis , Christoph Lange , Oya Beyan , Zeyd Boukhers

FDO Summit Berlin 2024 Proceedings 5 1-2 2024

PDF DOI
Zoubia, O., Asundi, N. B., Dogan, S., Koumpis, A., Lange, C., Beyan, O., & Boukhers, Z. (2024). FDO manager: minimum viable FAIR digital object implementation. FDO Summit Berlin 2024 Proceedings, 5, 1–2. https://doi.org/10.52825/ocp.v5i.1421

Zoubia, Oussama, , and Nagaraj Bahubali Asundi, , and Sezin Dogan, , and Adamantios Koumpis, , and Christoph Lange, , and Oya Beyan, , and Zeyd Boukhers. "FDO manager: minimum viable FAIR digital object implementation." FDO Summit Berlin 2024 Proceedings, vol. 5, 2024, pp. 1-2.

Zoubia, Oussama, , and Nagaraj Bahubali Asundi, , and Sezin Dogan, , and Adamantios Koumpis, , and Christoph Lange, , and Oya Beyan, , and Zeyd Boukhers. "FDO manager: minimum viable FAIR digital object implementation." FDO Summit Berlin 2024 Proceedings 5 (2024): 1-2.
@inproceedings{Zoubia2024fdo, title = {FDO manager: minimum viable FAIR digital object implementation}, author = {Oussama Zoubia and Nagaraj Bahubali Asundi and Sezin Dogan and Adamantios Koumpis and Christoph Lange and Oya Beyan and Zeyd Boukhers}, booktitle = {FDO Summit Berlin 2024 Proceedings}, volume = {5}, pages = {1-2}, year = {2024}, doi = {10.52825/ocp.v5i.1421}, url = {https://doi.org/10.52825/ocp.v5i.1421}, publisher = {TIB Open Publishing}, }
In the digital age, data has emerged as one of the most valuable assets across various sectors, including academia, industry, and healthcare. Effective data preservation involves the management of data to ensure its long-term accessibility and usability. Given the importance and sensitivity of data, the need for effective management is a crucial necessity. One of the big recent proposed approaches for data management is the FAIR Digital Objects (FDOs) which has emerged to revolutionize the field of data management and preservation. Central to this revolution is the alignment of FDOs with the FAIR principles (Findable, Accessible, Interoperable, Reusable), particularly emphasizing machine-actionability and interoperability across diverse data ecosystems. This paper presents "FDO Manager," a Minimum Viable Implementation of FDOs, tailored specifically for the use case and field of research artefacts such as datasets, publications, and code. The paper discusses the core ideas behind the FDO Manager, its architecture, usage and implementation details, as well as its potential impact, demonstrating a simple and abstract implementation of FDOs in the research realm.

2023

Data trading and monetization: challenges and open research directions

Qusai Ramadan , Zeyd Boukhers , Muath AlShaikh , Christoph Lange , Jan Jürjens

The 7th International Conference on Future Networks and Distributed Systems 344-351 2023

PDF DOI
Ramadan, Q., Boukhers, Z., AlShaikh, M., Lange, C., & Jürjens, J. (2023). Data trading and monetization: challenges and open research directions. The 7th International Conference on Future Networks and Distributed Systems, 344–351. https://doi.org/10.1145/3644713.3644758

Ramadan, Qusai, , and Zeyd Boukhers, , and Muath AlShaikh, , and Christoph Lange, , and Jan Jürjens. "Data trading and monetization: challenges and open research directions." The 7th International Conference on Future Networks and Distributed Systems, 2023, pp. 344-351.

Ramadan, Qusai, , and Zeyd Boukhers, , and Muath AlShaikh, , and Christoph Lange, , and Jan Jürjens. "Data trading and monetization: challenges and open research directions." The 7th International Conference on Future Networks and Distributed Systems (2023): 344-351.
@inproceedings{Ramadan2023data, title = {Data trading and monetization: challenges and open research directions}, author = {Qusai Ramadan and Zeyd Boukhers and Muath AlShaikh and Christoph Lange and Jan Jürjens}, booktitle = {The 7th International Conference on Future Networks and Distributed Systems}, pages = {344-351}, year = {2023}, doi = {10.1145/3644713.3644758}, url = {https://doi.org/10.1145/3644713.3644758}, publisher = {ACM}, }
NFDI4DS Infrastructure and Services

Sonja Schimmler , Bianca Wentzel , Arnim Bleier , Stefan Dietze , Saurav Karmakar , Peter Mutschke , Angelie Kraft , Tilahun A Taffa , Ricardo Usbeck , Zeyd Boukhers , Sören Auer , Leyla J Castro , Marcel R Ackermann , Thomas Neumuth , Daniel Schneider , Ziawasch Abedjan , Atif Latif , Fidan Limani , Raia Abu Ahmad , Georg Rehm , Sima Attar Khorasani , Matthias Lieber

INFORMATIK 2023 - Designing Futures: Zukünfte gestalten 2023

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Schimmler, S., Wentzel, B., Bleier, A., Dietze, S., Karmakar, S., Mutschke, P., Kraft, A., Taffa, T. A., Usbeck, R., Boukhers, Z., Auer, S., Castro, L. J., Ackermann, M. R., Neumuth, T., Schneider, D., Abedjan, Z., Latif, A., Limani, F., Ahmad, R. A., … Lieber, M. (2023, December 2). NFDI4DS Infrastructure and Services. INFORMATIK 2023 - Designing Futures: Zukünfte Gestalten. https://doi.org/10.18420/inf2023_103

Schimmler, Sonja, , and Bianca Wentzel, , and Arnim Bleier, , and Stefan Dietze, , and Saurav Karmakar, , and Peter Mutschke, , and Angelie Kraft, , and Tilahun A Taffa, , and Ricardo Usbeck, , and Zeyd Boukhers, , and Sören Auer, , and Leyla J Castro, , and Marcel R Ackermann, , and Thomas Neumuth, , and Daniel Schneider, , and Ziawasch Abedjan, , and Atif Latif, , and Fidan Limani, , and Raia Abu Ahmad, , and Georg Rehm, , and Sima Attar Khorasani, , and Matthias Lieber. "NFDI4DS Infrastructure and Services." INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023.

Schimmler, Sonja, , and Bianca Wentzel, , and Arnim Bleier, , and Stefan Dietze, , and Saurav Karmakar, , and Peter Mutschke, , and Angelie Kraft, , and Tilahun A Taffa, , and Ricardo Usbeck, , and Zeyd Boukhers, , and Sören Auer, , and Leyla J Castro, , and Marcel R Ackermann, , and Thomas Neumuth, , and Daniel Schneider, , and Ziawasch Abedjan, , and Atif Latif, , and Fidan Limani, , and Raia Abu Ahmad, , and Georg Rehm, , and Sima Attar Khorasani, , and Matthias Lieber. "NFDI4DS Infrastructure and Services." INFORMATIK 2023 - Designing Futures: Zukünfte gestalten (2023).
@inproceedings{Schimmler2023nfdi4ds, title = {NFDI4DS Infrastructure and Services}, author = {Sonja Schimmler and Bianca Wentzel and Arnim Bleier and Stefan Dietze and Saurav Karmakar and Peter Mutschke and Angelie Kraft and Tilahun A Taffa and Ricardo Usbeck and Zeyd Boukhers and Sören Auer and Leyla J Castro and Marcel R Ackermann and Thomas Neumuth and Daniel Schneider and Ziawasch Abedjan and Atif Latif and Fidan Limani and Raia Abu Ahmad and Georg Rehm and Sima Attar Khorasani and Matthias Lieber}, booktitle = {INFORMATIK 2023 - Designing Futures: Zukünfte gestalten}, year = {2023}, url = {https://doi.org/10.18420/inf2023_103}, }
Research knowledge graphs in NFDI4DS

Saurav Karmakar , Matthäus Zloch , Fidan Limani , Benjamin Zapilko , Sharmila Upadhyaya , Jennifer D’Souza , Leyla J Castro , Georg Rehm , Marcel R Ackermann , Harald Sack , Zeyd Boukhers , Sonja Schimmler , Danilo Dessí , Peter Mutschke , Stefan Dietze

INFORMATIK 2023 - Designing Futures: Zukünfte gestalten 2023

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Karmakar, S., Zloch, M., Limani, F., Zapilko, B., Upadhyaya, S., D’Souza, J., Castro, L. J., Rehm, G., Ackermann, M. R., Sack, H., Boukhers, Z., Schimmler, S., Dessí, D., Mutschke, P., & Dietze, S. (2023, December 2). Research knowledge graphs in NFDI4DS. INFORMATIK 2023 - Designing Futures: Zukünfte Gestalten. https://doi.org/10.18420/inf2023_102

Karmakar, Saurav, , and Matthäus Zloch, , and Fidan Limani, , and Benjamin Zapilko, , and Sharmila Upadhyaya, , and Jennifer D’Souza, , and Leyla J Castro, , and Georg Rehm, , and Marcel R Ackermann, , and Harald Sack, , and Zeyd Boukhers, , and Sonja Schimmler, , and Danilo Dessí, , and Peter Mutschke, , and Stefan Dietze. "Research knowledge graphs in NFDI4DS." INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023.

Karmakar, Saurav, , and Matthäus Zloch, , and Fidan Limani, , and Benjamin Zapilko, , and Sharmila Upadhyaya, , and Jennifer D’Souza, , and Leyla J Castro, , and Georg Rehm, , and Marcel R Ackermann, , and Harald Sack, , and Zeyd Boukhers, , and Sonja Schimmler, , and Danilo Dessí, , and Peter Mutschke, , and Stefan Dietze. "Research knowledge graphs in NFDI4DS." INFORMATIK 2023 - Designing Futures: Zukünfte gestalten (2023).
@inproceedings{Karmakar2023research, title = {Research knowledge graphs in NFDI4DS}, author = {Saurav Karmakar and Matthäus Zloch and Fidan Limani and Benjamin Zapilko and Sharmila Upadhyaya and Jennifer D’Souza and Leyla J Castro and Georg Rehm and Marcel R Ackermann and Harald Sack and Zeyd Boukhers and Sonja Schimmler and Danilo Dessí and Peter Mutschke and Stefan Dietze}, booktitle = {INFORMATIK 2023 - Designing Futures: Zukünfte gestalten}, year = {2023}, url = {https://doi.org/10.18420/inf2023_102}, }
FAIR Data Spaces - Building a Common Cloud-Based Data Space for Industry and Science by Linking Gaia-X and NFDI

Zeyd Boukhers , Daniela Mockler

1st Conference on Research Data Infrastructure (CoRDI) - Connecting Communities 2023

PDF DOI
Boukhers, Z., & Mockler, D. (2023, September 1). FAIR Data Spaces - Building a Common Cloud-Based Data Space for Industry and Science by Linking Gaia-X and NFDI. 1st Conference on Research Data Infrastructure (CoRDI) - Connecting Communities. https://doi.org/10.5281/zenodo.8409643

Boukhers, Zeyd, , and Daniela Mockler. "FAIR Data Spaces - Building a Common Cloud-Based Data Space for Industry and Science by Linking Gaia-X and NFDI." 1st Conference on Research Data Infrastructure (CoRDI) - Connecting Communities, 2023.

Boukhers, Zeyd, , and Daniela Mockler. "FAIR Data Spaces - Building a Common Cloud-Based Data Space for Industry and Science by Linking Gaia-X and NFDI." 1st Conference on Research Data Infrastructure (CoRDI) - Connecting Communities (2023).
@inproceedings{Boukhers2023fair, title = {FAIR Data Spaces - Building a Common Cloud-Based Data Space for Industry and Science by Linking Gaia-X and NFDI}, author = {Zeyd Boukhers and Daniela Mockler}, booktitle = {1st Conference on Research Data Infrastructure (CoRDI) - Connecting Communities}, year = {2023}, doi = {10.5281/zenodo.8409643}, url = {https://zeyd.boukhers.com/wp-content/uploads/2023/12/FAIR-Data-Spaces-Posterabstract-CoRDI-2023.pdf}, }
Beyond trading data: the hidden influence of public awareness and interest on cryptocurrency volatility

Zeyd Boukhers , Azeddine Bouabdellah , Cong Yang , Jan Jürjens

32nd ACM International Conference on Information and Knowledge Management (CIKM) 142-151 2023

PDF DOI
Boukhers, Z., Bouabdellah, A., Yang, C., & Jürjens, J. (2023). Beyond trading data: the hidden influence of public awareness and interest on cryptocurrency volatility. 32nd ACM International Conference on Information and Knowledge Management (CIKM), 142–151. https://doi.org/10.1145/3583780.3614790

Boukhers, Zeyd, , and Azeddine Bouabdellah, , and Cong Yang, , and Jan Jürjens. "Beyond trading data: the hidden influence of public awareness and interest on cryptocurrency volatility." 32nd ACM International Conference on Information and Knowledge Management (CIKM), 2023, pp. 142-151.

Boukhers, Zeyd, , and Azeddine Bouabdellah, , and Cong Yang, , and Jan Jürjens. "Beyond trading data: the hidden influence of public awareness and interest on cryptocurrency volatility." 32nd ACM International Conference on Information and Knowledge Management (CIKM) (2023): 142-151.
@inproceedings{Bukhers2023beyond, title = {Beyond trading data: the hidden influence of public awareness and interest on cryptocurrency volatility}, author = {Zeyd Boukhers and Azeddine Bouabdellah and Cong Yang and Jan Jürjens}, booktitle = {32nd ACM International Conference on Information and Knowledge Management (CIKM)}, pages = {142-151}, year = {2023}, doi = {10.1145/3583780.3614790}, url = {https://doi.org/10.1145/3583780.3614790}, publisher = {ACM}, }
Enhancing Reproducibility in Research through FAIR Digital Objects

FAIR principles FAIR Digital Objects reproducibility research artifacts data management NFDI4DataScience

Zeyd Boukhers , Leyla Jael Garcia Castro

1st Conference on Research Data Infrastructure 1 2023

PDF DOI
Boukhers, Z., & Castro, L. J. G. (2023). Enhancing Reproducibility in Research through FAIR Digital Objects. 1st Conference on Research Data Infrastructure, 1. https://doi.org/10.52825/cordi.v1i.406

Boukhers, Zeyd, , and Leyla Jael Garcia Castro. "Enhancing Reproducibility in Research through FAIR Digital Objects." 1st Conference on Research Data Infrastructure, vol. 1, 2023.

Boukhers, Zeyd, , and Leyla Jael Garcia Castro. "Enhancing Reproducibility in Research through FAIR Digital Objects." 1st Conference on Research Data Infrastructure 1 (2023).
@inproceedings{Boukhers2023enhancing, title = {Enhancing Reproducibility in Research through FAIR Digital Objects}, author = {Zeyd Boukhers and Leyla Jael Garcia Castro}, booktitle = {1st Conference on Research Data Infrastructure}, volume = {1}, year = {2023}, doi = {10.52825/cordi.v1i.406}, url = {https://doi.org/10.52825/cordi.v1i.406}, publisher = {TIB Open Publishing}, }
The FAIR principles were introduced to enhance data reuse by providing guidelines for effective data management practices. In the broader context of research, assets encompass not only data but also artifacts such as code, software, and publications. FAIRifying these artifacts is as essential as FAIRifying data, given the increasing complexity of current AI approaches that make reproducibility extremely challenging. Therefore, the reuse of these artifacts is growing in importance. The concept of FAIR Digital Objects (FDOs) presents a solution to FAIRify these artifacts, treating them as FDOs. NFDI4DataScience is embracing FDOs and proposing an architecture to efficiently manage them.
PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences

Zeyd Boukhers , Arnim Bleier , Yeliz Üçer Yediel , Mio Hienstorfer-Heitmann , Mehrshad Jaberansary , Adamantios Koumpis , Oya Beyan

ACM/IEEE JCDL 2023 - Joint Conference on Digital Libraries 251-252 2023

PDF DOI
Boukhers, Z., Bleier, A., Yediel, Y. Ü., Hienstorfer-Heitmann, M., Jaberansary, M., Koumpis, A., & Beyan, O. (2023). PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences. ACM/IEEE JCDL 2023 - Joint Conference on Digital Libraries, 251–252. https://doi.org/10.1109/jcdl57899.2023.00047

Boukhers, Zeyd, , and Arnim Bleier, , and Yeliz Üçer Yediel, , and Mio Hienstorfer-Heitmann, , and Mehrshad Jaberansary, , and Adamantios Koumpis, , and Oya Beyan. "PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences." ACM/IEEE JCDL 2023 - Joint Conference on Digital Libraries, 2023, pp. 251-252.

Boukhers, Zeyd, , and Arnim Bleier, , and Yeliz Üçer Yediel, , and Mio Hienstorfer-Heitmann, , and Mehrshad Jaberansary, , and Adamantios Koumpis, , and Oya Beyan. "PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences." ACM/IEEE JCDL 2023 - Joint Conference on Digital Libraries (2023): 251-252.
@inproceedings{Boukhers2023padme, title = {PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences}, author = {Zeyd Boukhers and Arnim Bleier and Yeliz Üçer Yediel and Mio Hienstorfer-Heitmann and Mehrshad Jaberansary and Adamantios Koumpis and Oya Beyan}, booktitle = {ACM/IEEE JCDL 2023 - Joint Conference on Digital Libraries}, pages = {251-252}, year = {2023}, doi = {10.1109/jcdl57899.2023.00047}, url = {https://doi.org/10.1109/jcdl57899.2023.00047}, publisher = {IEEE}, }
Enhancing data space semantic interoperability through Machine Learning: a visionary perspective

Zeyd Boukhers , Oya Beyan , Christoph Lange

Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion) 1462-1467 2023

PDF DOI
Boukhers, Z., Beyan, O., & Lange, C. (2023). Enhancing data space semantic interoperability through Machine Learning: a visionary perspective. Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion), 1462–1467. https://doi.org/10.1145/3543873.3587658

Boukhers, Zeyd, , and Oya Beyan, , and Christoph Lange. "Enhancing data space semantic interoperability through Machine Learning: a visionary perspective." Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion), 2023, pp. 1462-1467.

Boukhers, Zeyd, , and Oya Beyan, , and Christoph Lange. "Enhancing data space semantic interoperability through Machine Learning: a visionary perspective." Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion) (2023): 1462-1467.
@inproceedings{Boukhers2023enhancingdata, title = {Enhancing data space semantic interoperability through Machine Learning: a visionary perspective}, author = {Zeyd Boukhers and Oya Beyan and Christoph Lange}, booktitle = {Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion)}, pages = {1462-1467}, year = {2023}, doi = {10.1145/3543873.3587658}, url = {https://doi.org/10.1145/3543873.3587658}, publisher = {ACM}, }

2022

Whois? Deep author name disambiguation using bibliographic data

Zeyd Boukhers , Nagaraj Bahubali

The 26th International Conference on Theory and Practice of Digital Libraries 201-215 2022

PDF DOI
Boukhers, Z., & Bahubali, N. (2022). Whois? Deep author name disambiguation using bibliographic data. The 26th International Conference on Theory and Practice of Digital Libraries, 201–215. https://doi.org/10.1007/978-3-031-16802-4_16

Boukhers, Zeyd, , and Nagaraj Bahubali. "Whois? Deep author name disambiguation using bibliographic data." The 26th International Conference on Theory and Practice of Digital Libraries, 2022, pp. 201-215.

Boukhers, Zeyd, , and Nagaraj Bahubali. "Whois? Deep author name disambiguation using bibliographic data." The 26th International Conference on Theory and Practice of Digital Libraries (2022): 201-215.
@inproceedings{Boukhers2022whois, title = {Whois? Deep author name disambiguation using bibliographic data}, author = {Zeyd Boukhers and Nagaraj Bahubali}, booktitle = {The 26th International Conference on Theory and Practice of Digital Libraries}, pages = {201-215}, year = {2022}, doi = {10.1007/978-3-031-16802-4_16}, url = {https://doi.org/10.1007/978-3-031-16802-4_16}, publisher = {Springer}, }
Vision and Natural Language for Metadata Extraction from PDF Documents: A Multimodal Approach

Zeyd Boukhers , Azeddine Bouabdallah

ACM/IEEE Joint Conference on Digital Library 1-5 2022

PDF DOI
Boukhers, Z., & Bouabdallah, A. (2022). Vision and Natural Language for Metadata Extraction from PDF Documents: A Multimodal Approach. ACM/IEEE Joint Conference on Digital Library, 1–5. https://doi.org/10.1145/3529372.3533295

Boukhers, Zeyd, , and Azeddine Bouabdallah. "Vision and Natural Language for Metadata Extraction from PDF Documents: A Multimodal Approach." ACM/IEEE Joint Conference on Digital Library, 2022, pp. 1-5.

Boukhers, Zeyd, , and Azeddine Bouabdallah. "Vision and Natural Language for Metadata Extraction from PDF Documents: A Multimodal Approach." ACM/IEEE Joint Conference on Digital Library (2022): 1-5.
@inproceedings{Boukhers2022vision, title = {Vision and Natural Language for Metadata Extraction from PDF Documents: A Multimodal Approach}, author = {Zeyd Boukhers and Azeddine Bouabdallah}, booktitle = {ACM/IEEE Joint Conference on Digital Library}, pages = {1-5}, year = {2022}, doi = {10.1145/3529372.3533295}, url = {https://doi.org/10.1145/3529372.3533295}, publisher = {ACM}, }

2021

MexPub: Deep transfer learning for metadata extraction from German publications

Zeyd Boukhers , Prantik Goswami , Nada Beili , Muhammad Arslan Zafar , Timo Hartmann

ACM/IEEE Joint Conference on Digital Library 1-8 2021

PDF DOI
Boukhers, Z., Goswami, P., Beili, N., Zafar, M. A., & Hartmann, T. (2021). MexPub: Deep transfer learning for metadata extraction from German publications. ACM/IEEE Joint Conference on Digital Library, 1–8. https://doi.org/10.1109/jcdl52503.2021.00076

Boukhers, Zeyd, , and Prantik Goswami, , and Nada Beili, , and Muhammad Arslan Zafar, , and Timo Hartmann. "MexPub: Deep transfer learning for metadata extraction from German publications." ACM/IEEE Joint Conference on Digital Library, 2021, pp. 1-8.

Boukhers, Zeyd, , and Prantik Goswami, , and Nada Beili, , and Muhammad Arslan Zafar, , and Timo Hartmann. "MexPub: Deep transfer learning for metadata extraction from German publications." ACM/IEEE Joint Conference on Digital Library (2021): 1-8.
@inproceedings{Boukhers2021mexpub, title = {MexPub: Deep transfer learning for metadata extraction from German publications}, author = {Zeyd Boukhers and Prantik Goswami and Nada Beili and Muhammad Arslan Zafar and Timo Hartmann}, booktitle = {ACM/IEEE Joint Conference on Digital Library}, pages = {1-8}, year = {2021}, doi = {10.1109/jcdl52503.2021.00076}, url = {https://doi.org/10.1109/jcdl52503.2021.00076}, publisher = {IEEE}, }
BiblioDAP’21: The 1st workshop on bibliographic data analysis and processing

Zeyd Boukhers , Philipp Mayr , Silvio Peroni

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 4110–4111 2021

PDF DOI
Boukhers, Z., Mayr, P., & Peroni, S. (2021). BiblioDAP’21: The 1st workshop on bibliographic data analysis and processing. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4110–4111. https://doi.org/10.1145/3447548.3469482

Boukhers, Zeyd, , and Philipp Mayr, , and Silvio Peroni. "BiblioDAP’21: The 1st workshop on bibliographic data analysis and processing." Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021, pp. 4110–4111.

Boukhers, Zeyd, , and Philipp Mayr, , and Silvio Peroni. "BiblioDAP’21: The 1st workshop on bibliographic data analysis and processing." Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2021): 4110–4111.
@inproceedings{Boukhers2021bibliodap, title = {BiblioDAP’21: The 1st workshop on bibliographic data analysis and processing}, author = {Zeyd Boukhers and Philipp Mayr and Silvio Peroni}, booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, pages = {4110–4111}, year = {2021}, doi = {10.1145/3447548.3469482}, url = {https://doi.org/10.1145/3447548.3469482}, publisher = {ACM}, }

2019

An end-to-end approach for extracting and segmenting high-variance references from PDF documents

Zeyd Boukhers , Shrihash Ambhore , Steffen Staab

ACM/IEEE Joint Conference on Digital Library 1-10 2019

PDF DOI
Boukhers, Z., Ambhore, S., & Staab, S. (2019). An end-to-end approach for extracting and segmenting high-variance references from PDF documents. ACM/IEEE Joint Conference on Digital Library, 1–10. https://doi.org/10.1109/jcdl.2019.00035

Boukhers, Zeyd, , and Shrihash Ambhore, , and Steffen Staab. "An end-to-end approach for extracting and segmenting high-variance references from PDF documents." ACM/IEEE Joint Conference on Digital Library, 2019, pp. 1-10.

Boukhers, Zeyd, , and Shrihash Ambhore, , and Steffen Staab. "An end-to-end approach for extracting and segmenting high-variance references from PDF documents." ACM/IEEE Joint Conference on Digital Library (2019): 1-10.
@inproceedings{Boukhers2019an, title = {An end-to-end approach for extracting and segmenting high-variance references from PDF documents}, author = {Zeyd Boukhers and Shrihash Ambhore and Steffen Staab}, booktitle = {ACM/IEEE Joint Conference on Digital Library}, pages = {1-10}, year = {2019}, doi = {10.1109/jcdl.2019.00035}, url = {https://doi.org/10.1109/jcdl.2019.00035}, publisher = {ACM}, }
EXCITE – A toolchain to extract, match and publish open literature references

Azam Hosseini , Behnam Ghavimi , Zeyd Boukhers , Philipp Mayr

ACM/IEEE Joint Conference on Digital Library 432-433 2019

PDF DOI
Hosseini, A., Ghavimi, B., Boukhers, Z., & Mayr, P. (2019). EXCITE – A toolchain to extract, match and publish open literature references. ACM/IEEE Joint Conference on Digital Library, 432–433. https://doi.org/10.1109/jcdl.2019.00105

Hosseini, Azam, , and Behnam Ghavimi, , and Zeyd Boukhers, , and Philipp Mayr. "EXCITE – A toolchain to extract, match and publish open literature references." ACM/IEEE Joint Conference on Digital Library, 2019, pp. 432-433.

Hosseini, Azam, , and Behnam Ghavimi, , and Zeyd Boukhers, , and Philipp Mayr. "EXCITE – A toolchain to extract, match and publish open literature references." ACM/IEEE Joint Conference on Digital Library (2019): 432-433.
@inproceedings{Hosseini2019excite, title = {EXCITE – A toolchain to extract, match and publish open literature references}, author = {Azam Hosseini and Behnam Ghavimi and Zeyd Boukhers and Philipp Mayr}, booktitle = {ACM/IEEE Joint Conference on Digital Library}, pages = {432-433}, year = {2019}, doi = {10.1109/jcdl.2019.00105}, url = {https://doi.org/10.1109/jcdl.2019.00105}, publisher = {ACM}, }

2016

Automatic recognition of movement patterns in the Vojta therapy using RGB-D data

Muhammad Hassan Khan , Jullien Helsper , Zeyd Boukhers , Marcin Grzegorzek

Image Processing (ICIP), IEEE International Conference on 1235-1239 2016

PDF DOI
Khan, M. H., Helsper, J., Boukhers, Z., & Grzegorzek, M. (2016). Automatic recognition of movement patterns in the Vojta therapy using RGB-D data. Image Processing (ICIP), IEEE International Conference On, 1235–1239. https://doi.org/10.1109/icip.2016.7532555

Khan, Muhammad Hassan, , and Jullien Helsper, , and Zeyd Boukhers, , and Marcin Grzegorzek. "Automatic recognition of movement patterns in the Vojta therapy using RGB-D data." Image Processing (ICIP), IEEE International Conference on, 2016, pp. 1235-1239.

Khan, Muhammad Hassan, , and Jullien Helsper, , and Zeyd Boukhers, , and Marcin Grzegorzek. "Automatic recognition of movement patterns in the Vojta therapy using RGB-D data." Image Processing (ICIP), IEEE International Conference on (2016): 1235-1239.
@inproceedings{Khan2016automatic, title = {Automatic recognition of movement patterns in the Vojta therapy using RGB-D data}, author = {Muhammad Hassan Khan and Jullien Helsper and Zeyd Boukhers and Marcin Grzegorzek}, booktitle = {Image Processing (ICIP), IEEE International Conference on}, pages = {1235-1239}, year = {2016}, doi = {10.1109/icip.2016.7532555}, url = {https://doi.org/10.1109/icip.2016.7532555}, publisher = {IEEE}, }

2015

Shape-Based Eye Blinking Detection and Analysis

Zeyd Boukhers , Tomasz Jarzyński , Florian Schmidt , Oliver Tiebe , Marcin Grzegorzek

Computer Recognition Systems (CORES), International Conference on 327-335 2015

PDF DOI
Boukhers, Z., Jarzyński, T., Schmidt, F., Tiebe, O., & Grzegorzek, M. (2015). Shape-Based Eye Blinking Detection and Analysis. Computer Recognition Systems (CORES), International Conference On, 327–335. https://doi.org/10.1007/978-3-319-26227-7_31

Boukhers, Zeyd, , and Tomasz Jarzyński, , and Florian Schmidt, , and Oliver Tiebe, , and Marcin Grzegorzek. "Shape-Based Eye Blinking Detection and Analysis." Computer Recognition Systems (CORES), International Conference on, 2015, pp. 327-335.

Boukhers, Zeyd, , and Tomasz Jarzyński, , and Florian Schmidt, , and Oliver Tiebe, , and Marcin Grzegorzek. "Shape-Based Eye Blinking Detection and Analysis." Computer Recognition Systems (CORES), International Conference on (2015): 327-335.
@inproceedings{Boukhers2015shape, title = {Shape-Based Eye Blinking Detection and Analysis}, author = {Zeyd Boukhers and Tomasz Jarzyński and Florian Schmidt and Oliver Tiebe and Marcin Grzegorzek}, booktitle = {Computer Recognition Systems (CORES), International Conference on}, pages = {327-335}, year = {2015}, doi = {10.1007/978-3-319-26227-7_31}, url = {https://doi.org/10.1007/978-3-319-26227-7_31}, publisher = {Springer International Publishing}, }
Environmental microbiological content-based image retrieval system using internal structure histogram

Yan Ling Zou , Chen Li , Zeyd Boukhers , Kimiaki Shirahama , Tao Jiang , Marcin Grzegorzek

Computer Recognition Systems (CORES), International Conference on 543-552 2015

PDF DOI
Zou, Y. L., Li, C., Boukhers, Z., Shirahama, K., Jiang, T., & Grzegorzek, M. (2015). Environmental microbiological content-based image retrieval system using internal structure histogram. Computer Recognition Systems (CORES), International Conference On, 543–552. https://doi.org/10.1007/978-3-319-26227-7_51

Zou, Yan Ling, , and Chen Li, , and Zeyd Boukhers, , and Kimiaki Shirahama, , and Tao Jiang, , and Marcin Grzegorzek. "Environmental microbiological content-based image retrieval system using internal structure histogram." Computer Recognition Systems (CORES), International Conference on, 2015, pp. 543-552.

Zou, Yan Ling, , and Chen Li, , and Zeyd Boukhers, , and Kimiaki Shirahama, , and Tao Jiang, , and Marcin Grzegorzek. "Environmental microbiological content-based image retrieval system using internal structure histogram." Computer Recognition Systems (CORES), International Conference on (2015): 543-552.
@inproceedings{Ling2015enviromental, title = {Environmental microbiological content-based image retrieval system using internal structure histogram}, author = {Yan Ling Zou and Chen Li and Zeyd Boukhers and Kimiaki Shirahama and Tao Jiang and Marcin Grzegorzek}, booktitle = {Computer Recognition Systems (CORES), International Conference on}, pages = {543-552}, year = {2015}, doi = {10.1007/978-3-319-26227-7_51}, url = {https://doi.org/10.1007/978-3-319-26227-7_51}, publisher = {Springer International Publishing}, }
Extracting 3D Trajectories of Objects from 2D Videos Using Particle Filter

Zeyd Boukhers , Kimiaki Shirahama , Frédéric Li , Marcin Grzegorzek

Multimedia Retrieval, ACM International Conference on 83-90 2015

PDF DOI
Boukhers, Z., Shirahama, K., Li, F., & Grzegorzek, M. (2015). Extracting 3D Trajectories of Objects from 2D Videos Using Particle Filter. Multimedia Retrieval, ACM International Conference On, 83–90. https://doi.org/10.1145/2671188.2749331

Boukhers, Zeyd, , and Kimiaki Shirahama, , and Frédéric Li, , and Marcin Grzegorzek. "Extracting 3D Trajectories of Objects from 2D Videos Using Particle Filter." Multimedia Retrieval, ACM International Conference on, 2015, pp. 83-90.

Boukhers, Zeyd, , and Kimiaki Shirahama, , and Frédéric Li, , and Marcin Grzegorzek. "Extracting 3D Trajectories of Objects from 2D Videos Using Particle Filter." Multimedia Retrieval, ACM International Conference on (2015): 83-90.
@inproceedings{Boukhers2015exrtracting, title = {Extracting 3D Trajectories of Objects from 2D Videos Using Particle Filter}, author = {Zeyd Boukhers and Kimiaki Shirahama and Frédéric Li and Marcin Grzegorzek}, booktitle = {Multimedia Retrieval, ACM International Conference on}, pages = {83-90}, year = {2015}, doi = {10.1145/2671188.2749331}, url = {https://doi.org/10.1145/2671188.2749331}, publisher = {ACM}, }

Workshop Papers

2025

MESD: Metadata Extraction from Scholarly Documents - A Shared Task Overview

metadata extraction scholarly documents knowledge graphs shared task

Zeyd Boukhers , Cong Yang

ESWC International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs (NSLP 2025) 2025

PDF DOI
Boukhers, Z., & Yang, C. (2025, April). MESD: Metadata Extraction from Scholarly Documents - A Shared Task Overview. ESWC International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs (NSLP 2025). https://doi.org/00000

Boukhers, Zeyd, , and Cong Yang. "MESD: Metadata Extraction from Scholarly Documents - A Shared Task Overview." ESWC International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs (NSLP 2025), 2025.

Boukhers, Zeyd, , and Cong Yang. "MESD: Metadata Extraction from Scholarly Documents - A Shared Task Overview." ESWC International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs (NSLP 2025) (2025).
@inproceedings{Boukhers2025MESD, title = {MESD: Metadata Extraction from Scholarly Documents - A Shared Task Overview}, author = {Zeyd Boukhers and Cong Yang}, booktitle = {ESWC International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs (NSLP 2025)}, year = {2025}, doi = {00000}, url = {https://ceur-ws.org/Vol-3977/NSLP-06.pdf}, publisher = {ESWC Workshop Proceedings}, }
This paper presents an overview of the Metadata Extraction from Scholarly Documents (MESD) shared task, which was designed to address the challenge of extracting structured metadata (e.g. Title, Author, Abstract, etc.) from scientific publications. The task aimed to promote the development of techniques for making scholarly data more Findable, Accessible, Interoperable, Reusable (FAIR) by improving metadata extraction from PDF documents. We describe the task design and the creation of two complementary datasets: (1) the S2ORC_Exp500v1 dataset consisting of 500 training samples, 100 validation samples, and 100 test samples with text-based annotations, and (2) the SSOAR Multidisciplinary Vision Dataset (SSOARGMVD) containing more than 8000 documents with bounding box annotations suitable for computer vision approaches. We discuss potential directions for future research in metadata extraction from scholarly documents, highlighting the opportunities presented by these new resources.
Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper

fairness requirements specification verification software engineering ethics

Qusai Ramadan , Jukka Ruohonen , Abhishek Tiwari , Adam Alami , Zeyd Boukhers

2025 IEEE 33rd International Requirements Engineering Conference Workshops 2025

PDF DOI
Ramadan, Q., Ruohonen, J., Tiwari, A., Alami, A., & Boukhers, Z. (2025, September). Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper. 2025 IEEE 33rd International Requirements Engineering Conference Workshops. https://doi.org/10.1109/rew66121.2025.00059

Ramadan, Qusai, , and Jukka Ruohonen, , and Abhishek Tiwari, , and Adam Alami, , and Zeyd Boukhers. "Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper." 2025 IEEE 33rd International Requirements Engineering Conference Workshops, 2025.

Ramadan, Qusai, , and Jukka Ruohonen, , and Abhishek Tiwari, , and Adam Alami, , and Zeyd Boukhers. "Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper." 2025 IEEE 33rd International Requirements Engineering Conference Workshops (2025).
@inproceedings{Ramadan2025Towards, title = {Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper}, author = {Qusai Ramadan and Jukka Ruohonen and Abhishek Tiwari and Adam Alami and Zeyd Boukhers}, booktitle = {2025 IEEE 33rd International Requirements Engineering Conference Workshops}, year = {2025}, doi = {10.1109/rew66121.2025.00059}, url = {https://doi.org/10.1109/rew66121.2025.00059}, publisher = {IEEE}, }
Decisions suggested by improperly designed software systems might be prone to discriminate against people based on protected characteristics, such as gender and ethnicity. Previous studies attribute such undesired behavior to flaws in algorithmic design or biased data. However, these studies ignore that discrimination is often the result of a lack of well-specified fairness requirements and their verification. The fact that experts’ knowledge about fairness is often implicit makes the task of specifying precise and verifiable fairness requirements difficult. In related domains, such as security engineering, knowledge graphs have been proven to be effective in formalizing knowledge to assist requirements specification and verification. To address the lack of formal mechanisms for specifying and verifying fairness requirements, we propose the development of a knowledge graph-based framework for fairness. In this paper, we discuss the challenges, research questions, and a road map towards addressing the research questions.
A survey on metadata for Machine Learning models and datasets: standards, practices, and harmonization challenges

metadata machine learning models datasets standards harmonization survey

Asefa Genet , Chen Zongxiong , Oussama Zoubia , Fidan Limani , Kanishka Silva , Muhammad Asif Suryani , Benjamin Zapilko , Leyla Jael Castro , Ekaterina Kutafina , Dhwani Solanki , Heike Fliegl , Sonja Schimmler , Zeyd Boukhers , Harald Sack

Sci-K Workshop at the International Semantic Web Conference (ISWC 2025) 2025

PDF DOI
Genet, A., Zongxiong, C., Zoubia, O., Limani, F., Silva, K., Suryani, M. A., Zapilko, B., Castro, L. J., Kutafina, E., Solanki, D., Fliegl, H., Schimmler, S., Boukhers, Z., & Sack, H. (2025, September). A survey on metadata for Machine Learning models and datasets: standards, practices, and harmonization challenges. Sci-K Workshop at the International Semantic Web Conference (ISWC 2025). https://doi.org/10.24406/publica-5924

Genet, Asefa, , and Chen Zongxiong, , and Oussama Zoubia, , and Fidan Limani, , and Kanishka Silva, , and Muhammad Asif Suryani, , and Benjamin Zapilko, , and Leyla Jael Castro, , and Ekaterina Kutafina, , and Dhwani Solanki, , and Heike Fliegl, , and Sonja Schimmler, , and Zeyd Boukhers, , and Harald Sack. "A survey on metadata for Machine Learning models and datasets: standards, practices, and harmonization challenges." Sci-K Workshop at the International Semantic Web Conference (ISWC 2025), 2025.

Genet, Asefa, , and Chen Zongxiong, , and Oussama Zoubia, , and Fidan Limani, , and Kanishka Silva, , and Muhammad Asif Suryani, , and Benjamin Zapilko, , and Leyla Jael Castro, , and Ekaterina Kutafina, , and Dhwani Solanki, , and Heike Fliegl, , and Sonja Schimmler, , and Zeyd Boukhers, , and Harald Sack. "A survey on metadata for Machine Learning models and datasets: standards, practices, and harmonization challenges." Sci-K Workshop at the International Semantic Web Conference (ISWC 2025) (2025).
@inproceedings{Genet2025A, title = {A survey on metadata for Machine Learning models and datasets: standards, practices, and harmonization challenges}, author = {Asefa Genet and Chen Zongxiong and Oussama Zoubia and Fidan Limani and Kanishka Silva and Muhammad Asif Suryani and Benjamin Zapilko and Leyla Jael Castro and Ekaterina Kutafina and Dhwani Solanki and Heike Fliegl and Sonja Schimmler and Zeyd Boukhers and Harald Sack}, booktitle = {Sci-K Workshop at the International Semantic Web Conference (ISWC 2025)}, year = {2025}, doi = {10.24406/publica-5924}, url = {https://ceur-ws.org/Vol-4065/paper05.pdf}, publisher = {ISWC Workshop Proceedings}, }
The growing availability of machine learning (ML) models, datasets, and related artifacts across platforms, such as Hugging Face, GitHub, and Zenodo, has amplified the need for structured and standardized metadata. However, metadata practices remain highly heterogeneous, differing in schema design, vocabulary usage, and semantic expressiveness, posing significant challenges for tasks such as representation, extraction, alignment, and integration. This fragmentation impedes the development of infrastructures that depend on machine-actionable metadata to support discovery, provenance tracking, or cross-platform interoperability. While metadata is also foundational to enabling FAIR (Findable, Accessible, Interoperable, and Reusable) principles in ML, there is a lack of consolidated understanding of how existing standards support interoperability and alignment across platforms. In this survey, we review and compare a range of general-purpose and ML-specific metadata standards, evaluating their suitability for cross-platform alignment, discoverability, extensibility, and interoperability. We assess these standards based on defined criteria and analyze their potential to support unified, FAIR-compliant metadata infrastructures for ML, laying the groundwork for scalable and interoperable tooling in future ML ecosystems.
Multi-Objective Reinforcement Learning for Large Language Model Optimization: Visionary Perspective

reinforcement learning large language models optimization multi-objective RL

Lingxiao Kong , Cong Yang , Oya Beyan , Zeyd Boukhers

Multi-Objective Decision Making Workshop at ECAI 2025 2025

PDF DOI
Kong, L., Yang, C., Beyan, O., & Boukhers, Z. (2025, September). Multi-Objective Reinforcement Learning for Large Language Model Optimization: Visionary Perspective. Multi-Objective Decision Making Workshop at ECAI 2025. https://doi.org/arXiv.2509.21613

Kong, Lingxiao, , and Cong Yang, , and Oya Beyan, , and Zeyd Boukhers. "Multi-Objective Reinforcement Learning for Large Language Model Optimization: Visionary Perspective." Multi-Objective Decision Making Workshop at ECAI 2025, 2025.

Kong, Lingxiao, , and Cong Yang, , and Oya Beyan, , and Zeyd Boukhers. "Multi-Objective Reinforcement Learning for Large Language Model Optimization: Visionary Perspective." Multi-Objective Decision Making Workshop at ECAI 2025 (2025).
@inproceedings{Kong2025Multi-Objective, title = {Multi-Objective Reinforcement Learning for Large Language Model Optimization: Visionary Perspective}, author = {Lingxiao Kong and Cong Yang and Oya Beyan and Zeyd Boukhers}, booktitle = {Multi-Objective Decision Making Workshop at ECAI 2025}, year = {2025}, doi = {arXiv.2509.21613}, url = {https://arxiv.org/pdf/2509.21613}, publisher = {ECAI Workshop Proceedings}, }
Multi-Objective Reinforcement Learning (MORL) presents significant challenges and opportunities for optimizing multiple objectives in Large Language Models (LLMs). We introduce a MORL taxonomy and examine the advantages and limitations of various MORL methods when applied to LLM optimization, identifying the need for efficient and flexible approaches that accommodate personalization functionality and inherent complexities in LLMs and RL. We propose a vision for a MORL benchmarking framework that addresses the effects of different methods on diverse objective relationships. As future research directions, we focus on meta-policy MORL development that can improve efficiency and flexibility through its bi-level learning paradigm, highlighting key research questions and potential solutions for improving LLM performance.
FAIR Data Publisher: Strategie für den flächendeckenden Einsatz von FAIR Digital Objects

FAIR data FAIR Digital Objects data infrastructure data interoperability data reuse data accessibility research data management European data ecosystem

Marius Bensley , Philippe Calvez , Christian Backe , Patrick Draheim , Veit Briken , Anna Petschulies , Fabian Rack , Franziska Boehm , Jan Nagler , Christina Gillmann , Christoph Lange-Bever , Daham Mustafa , Joshua Gelhaar , Maik Mannsfeld , Michael Steinert , Zeyd Boukhers , Jana Böhm , Philipp Wieder , Sonja Sternkopf , Sven Bingert , Suat Can , Henrik Wörden , Insa Nüske , Sina Rohde , Timm Fitschen , Oliver Rack , Frank Tristram , Ashley Caselli , Philipp Essen , Tobias Kuhn , Virginia Balseiro , Ziroli Plutschow , Igor Garmaev , Marius Politze , Torben Miny , David Wagner , Hans-Günther Döbereiner , Lennard Scheurer , Lukas Schmidt , Malte Ohmstede , Robert Porzel , Torben Klarl , Mario Martini , Tom Schuchort , Nils Niendorf

Mission KI Report 2025

PDF DOI
Bensley, M., Calvez, P., Backe, C., Draheim, P., Briken, V., Petschulies, A., Rack, F., Boehm, F., Nagler, J., Gillmann, C., Lange-Bever, C., Mustafa, D., Gelhaar, J., Mannsfeld, M., Steinert, M., Boukhers, Z., Böhm, J., Wieder, P., Sternkopf, S., … Niendorf, N. (2025). FAIR Data Publisher: Strategie für den flächendeckenden Einsatz von FAIR Digital Objects. Mission KI Report. https://doi.org/10.24406/publica-6827

Bensley, Marius, , and Philippe Calvez, , and Christian Backe, , and Patrick Draheim, , and Veit Briken, , and Anna Petschulies, , and Fabian Rack, , and Franziska Boehm, , and Jan Nagler, , and Christina Gillmann, , and Christoph Lange-Bever, , and Daham Mustafa, , and Joshua Gelhaar, , and Maik Mannsfeld, , and Michael Steinert, , and Zeyd Boukhers, , and Jana Böhm, , and Philipp Wieder, , and Sonja Sternkopf, , and Sven Bingert, , and Suat Can, , and Henrik Wörden, , and Insa Nüske, , and Sina Rohde, , and Timm Fitschen, , and Oliver Rack, , and Frank Tristram, , and Ashley Caselli, , and Philipp Essen, , and Tobias Kuhn, , and Virginia Balseiro, , and Ziroli Plutschow, , and Igor Garmaev, , and Marius Politze, , and Torben Miny, , and David Wagner, , and Hans-Günther Döbereiner, , and Lennard Scheurer, , and Lukas Schmidt, , and Malte Ohmstede, , and Robert Porzel, , and Torben Klarl, , and Mario Martini, , and Tom Schuchort, , and Nils Niendorf. "FAIR Data Publisher: Strategie für den flächendeckenden Einsatz von FAIR Digital Objects." Mission KI Report, 2025.

Bensley, Marius, , and Philippe Calvez, , and Christian Backe, , and Patrick Draheim, , and Veit Briken, , and Anna Petschulies, , and Fabian Rack, , and Franziska Boehm, , and Jan Nagler, , and Christina Gillmann, , and Christoph Lange-Bever, , and Daham Mustafa, , and Joshua Gelhaar, , and Maik Mannsfeld, , and Michael Steinert, , and Zeyd Boukhers, , and Jana Böhm, , and Philipp Wieder, , and Sonja Sternkopf, , and Sven Bingert, , and Suat Can, , and Henrik Wörden, , and Insa Nüske, , and Sina Rohde, , and Timm Fitschen, , and Oliver Rack, , and Frank Tristram, , and Ashley Caselli, , and Philipp Essen, , and Tobias Kuhn, , and Virginia Balseiro, , and Ziroli Plutschow, , and Igor Garmaev, , and Marius Politze, , and Torben Miny, , and David Wagner, , and Hans-Günther Döbereiner, , and Lennard Scheurer, , and Lukas Schmidt, , and Malte Ohmstede, , and Robert Porzel, , and Torben Klarl, , and Mario Martini, , and Tom Schuchort, , and Nils Niendorf. "FAIR Data Publisher: Strategie für den flächendeckenden Einsatz von FAIR Digital Objects." Mission KI Report (2025).
@article{Bensley2025FAIRDataPublisher, title = {FAIR Data Publisher: Strategie für den flächendeckenden Einsatz von FAIR Digital Objects}, author = {Marius Bensley and Philippe Calvez and Christian Backe and Patrick Draheim and Veit Briken and Anna Petschulies and Fabian Rack and Franziska Boehm and Jan Nagler and Christina Gillmann and Christoph Lange-Bever and Daham Mustafa and Joshua Gelhaar and Maik Mannsfeld and Michael Steinert and Zeyd Boukhers and Jana Böhm and Philipp Wieder and Sonja Sternkopf and Sven Bingert and Suat Can and Henrik Wörden and Insa Nüske and Sina Rohde and Timm Fitschen and Oliver Rack and Frank Tristram and Ashley Caselli and Philipp Essen and Tobias Kuhn and Virginia Balseiro and Ziroli Plutschow and Igor Garmaev and Marius Politze and Torben Miny and David Wagner and Hans-Günther Döbereiner and Lennard Scheurer and Lukas Schmidt and Malte Ohmstede and Robert Porzel and Torben Klarl and Mario Martini and Tom Schuchort and Nils Niendorf}, journal = {Mission KI Report}, year = {2025}, doi = {10.24406/publica-6827}, url = {https://mission-ki.de/media/pages/files/e6eb8545eb-1763376569/fair-data-publisher_bericht_ds.pdf}, }
Der FAIR Data Publisher stellt eine zentrale technologische Initiative dar, um die Auffindbarkeit, Zugänglichkeit, Interoperabilität und Nachnutzbarkeit von Daten bedeutend zu verbessern. Dieses Projekt ist nicht nur ein technisches Werkzeug, sondern trägt zu einer wichtigen Entwicklung in der deutschen und europäischen Dateninfrastruktur bei, die von einem Konsortium aus mehreren namhaften Organisationen gemeinsam vorangetrieben wird.

2024

Falcon 7b for software mention detection in scholarly documents

software mention detection scholarly publications natural language processing large language models class imbalance adaptive sampling

Ameerali Khan , Qusai Ramadan , Cong Yang , Zeyd Boukhers

ESWC Workshop on Natural Scientific Language Processing and Research Knowledge Graphs 2024

PDF DOI
Khan, A., Ramadan, Q., Yang, C., & Boukhers, Z. (2024, June). Falcon 7b for software mention detection in scholarly documents. ESWC Workshop on Natural Scientific Language Processing and Research Knowledge Graphs. https://doi.org/10.1007/978-3-031-65794-8_20

Khan, Ameerali, , and Qusai Ramadan, , and Cong Yang, , and Zeyd Boukhers. "Falcon 7b for software mention detection in scholarly documents." ESWC Workshop on Natural Scientific Language Processing and Research Knowledge Graphs, 2024.

Khan, Ameerali, , and Qusai Ramadan, , and Cong Yang, , and Zeyd Boukhers. "Falcon 7b for software mention detection in scholarly documents." ESWC Workshop on Natural Scientific Language Processing and Research Knowledge Graphs (2024).
@inproceedings{Khan2024Falcon, title = {Falcon 7b for software mention detection in scholarly documents}, author = {Ameerali Khan and Qusai Ramadan and Cong Yang and Zeyd Boukhers}, booktitle = {ESWC Workshop on Natural Scientific Language Processing and Research Knowledge Graphs}, year = {2024}, doi = {10.1007/978-3-031-65794-8_20}, url = {https://doi.org/10.1007/978-3-031-65794-8_20}, publisher = {Springer}, }
This paper investigates applying Falcon-7b (a large language model) for detecting and classifying software mentions in scholarly texts. Focusing on Subtask I of Software Mention Detection in Scholarly Publications (SOMD), the authors explore training strategies including a dual-classifier approach, adaptive sampling, and weighted loss scaling to improve detection accuracy despite class imbalance and the complex syntax of academic writing. Their experiments demonstrate that selective labelling and adaptive sampling yield improved performance — though combining all strategies does not necessarily aggregate gains — and highlight challenges in using LLMs for scholarly text analysis.

2023

Pedestrian collision prediction using a monocular camera

pedestrian detection collision prediction monocular camera ADAS 3D detection computer vision

Shiyuan Chen , Xue Qin , Zeyd Boukhers , John See , Wei Sui , Cong Yang

8th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (iWOAR 2023) 2023

PDF DOI
Chen, S., Qin, X., Boukhers, Z., See, J., Sui, W., & Yang, C. (2023, October). Pedestrian collision prediction using a monocular camera. 8th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (IWOAR 2023). https://doi.org/10.1145/3615834.3615846

Chen, Shiyuan, , and Xue Qin, , and Zeyd Boukhers, , and John See, , and Wei Sui, , and Cong Yang. "Pedestrian collision prediction using a monocular camera." 8th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (iWOAR 2023), 2023.

Chen, Shiyuan, , and Xue Qin, , and Zeyd Boukhers, , and John See, , and Wei Sui, , and Cong Yang. "Pedestrian collision prediction using a monocular camera." 8th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (iWOAR 2023) (2023).
@inproceedings{Chen2023Pedestrian, title = {Pedestrian collision prediction using a monocular camera}, author = {Shiyuan Chen and Xue Qin and Zeyd Boukhers and John See and Wei Sui and Cong Yang}, booktitle = {8th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (iWOAR 2023)}, year = {2023}, doi = {10.1145/3615834.3615846}, url = {https://doi.org/10.1145/3615834.3615846}, publisher = {ACM}, }
This paper introduces a simple yet efficient method, PedView, for pedestrian collision warning in Advanced Driver Assistance Systems (ADAS). In contrast to existing approaches that rely on LiDAR and stereo cameras for pedestrian-vehicle distance calculation, our proposed PedView leverages a forward-looking monocular camera for 3D pedestrian detection — suitable for resource-limited environments like dealer-installed ADAS. PedView not only estimates distance and speed but also derives pedestrian location and intent via a 3D detector and fuzzy rules, enabling end-to-end collision prediction. Experiments on two datasets demonstrate significant improvements vs. discrete conditional rules (Precision 0.937 vs. 0.844, Recall 0.835 vs. 0.746), highlighting robustness across real-world scenarios.

2022

Evaluation scheme of FAIRness in scholarly data

FAIR data scholarly metadata open citations data evaluation data quality

Zeyd Boukhers

Workshop on Open Citations and Open Scholarly Metadata 2022

PDF DOI
Boukhers, Z. (2022, July). Evaluation scheme of FAIRness in scholarly data. Workshop on Open Citations and Open Scholarly Metadata. https://doi.org/10.5281/zenodo.7133290

Boukhers, Zeyd. "Evaluation scheme of FAIRness in scholarly data." Workshop on Open Citations and Open Scholarly Metadata, 2022.

Boukhers, Zeyd. "Evaluation scheme of FAIRness in scholarly data." Workshop on Open Citations and Open Scholarly Metadata (2022).
@inproceedings{Boukhers2022Evaluation, title = {Evaluation scheme of FAIRness in scholarly data}, author = {Zeyd Boukhers}, booktitle = {Workshop on Open Citations and Open Scholarly Metadata}, year = {2022}, doi = {10.5281/zenodo.7133290}, url = {https://doi.org/10.5281/zenodo.7133290}, publisher = {Zenodo / Workshop Proceedings}, }
NFDI4DataScience Registry for Reproducible Data Science and Artificial Intelligence

reproducible research FAIR data data infrastructure metadata knowledge graphs data science AI

Dietrich Rebholz-Schuhmann , Leyla Jael Castro , Zeyd Boukhers , Olga Giraldo , Adamantios Koumpis , Oya Beyan

Workshop on Open Citations and Open Scholarly Metadata 2022

PDF DOI
Rebholz-Schuhmann, D., Castro, L. J., Boukhers, Z., Giraldo, O., Koumpis, A., & Beyan, O. (2022, July). NFDI4DataScience Registry for Reproducible Data Science and Artificial Intelligence. Workshop on Open Citations and Open Scholarly Metadata. https://doi.org/10.5281/zenodo.7129715

Rebholz-Schuhmann, Dietrich, , and Leyla Jael Castro, , and Zeyd Boukhers, , and Olga Giraldo, , and Adamantios Koumpis, , and Oya Beyan. "NFDI4DataScience Registry for Reproducible Data Science and Artificial Intelligence." Workshop on Open Citations and Open Scholarly Metadata, 2022.

Rebholz-Schuhmann, Dietrich, , and Leyla Jael Castro, , and Zeyd Boukhers, , and Olga Giraldo, , and Adamantios Koumpis, , and Oya Beyan. "NFDI4DataScience Registry for Reproducible Data Science and Artificial Intelligence." Workshop on Open Citations and Open Scholarly Metadata (2022).
@inproceedings{Rebholz-Schuhmann2022NFDI4DataScience, title = {NFDI4DataScience Registry for Reproducible Data Science and Artificial Intelligence}, author = {Dietrich Rebholz-Schuhmann and Leyla Jael Castro and Zeyd Boukhers and Olga Giraldo and Adamantios Koumpis and Oya Beyan}, booktitle = {Workshop on Open Citations and Open Scholarly Metadata}, year = {2022}, doi = {10.5281/zenodo.7129715}, url = {https://doi.org/10.5281/zenodo.7129715}, publisher = {Zenodo / Workshop Proceedings}, }
The paper describes the vision and objectives of the NFDI4DataScience (NFDI4DS) consortium: to support the entire research data lifecycle for data science and AI in Germany. It proposes infrastructure for storage and sharing of data, code, models, metadata, and knowledge graphs to support reproducibility, transparency, and compliance with FAIR principles.

2021

LaHAR: Latent Human Activity Recognition using LDA

human activity recognition HAR unsupervised learning LDA sensory data sensor-based HAR

Zeyd Boukhers , Danniene Wete , Steffen Staab

The AAAI 2021 Workshop on Plan, Activity, and Intent Recognition 2021

PDF
Boukhers, Z., Wete, D., & Staab, S. (2021, February). LaHAR: Latent Human Activity Recognition using LDA. The AAAI 2021 Workshop on Plan, Activity, and Intent Recognition. https://arxiv.org/abs/2011.11151

Boukhers, Zeyd, , and Danniene Wete, , and Steffen Staab. "LaHAR: Latent Human Activity Recognition using LDA." The AAAI 2021 Workshop on Plan, Activity, and Intent Recognition, 2021.

Boukhers, Zeyd, , and Danniene Wete, , and Steffen Staab. "LaHAR: Latent Human Activity Recognition using LDA." The AAAI 2021 Workshop on Plan, Activity, and Intent Recognition (2021).
@inproceedings{Boukhers2021LaHAR, title = {LaHAR: Latent Human Activity Recognition using LDA}, author = {Zeyd Boukhers and Danniene Wete and Steffen Staab}, booktitle = {The AAAI 2021 Workshop on Plan, Activity, and Intent Recognition}, year = {2021}, url = {https://arxiv.org/abs/2011.11151}, publisher = {AAAI Workshop Proceedings}, }
Processing sequential multi-sensor data becomes important in many tasks due to the dramatic increase in the availability of sensors that can acquire sequential data over time. Human Activity Recognition (HAR) is one such field. Unlike most approaches which use predefined activity classes, this paper proposes a novel approach to discover latent HAR patterns in sequential data. We employ Latent Dirichlet Allocation (LDA) — originally a topic-modeling method — applied to “sensory words” extracted from the sequential sensor data, turning sensor sequences into Bag-of-Words representations. Experiments on a challenging HAR dataset show that LDA can uncover underlying structures in the data, producing human-understandable representations; extrinsic evaluations demonstrated accurate clustering of sequences comparable to labelled activities.
Early detection of COVID lies without evidences and propagation information

Ipek Baris , Zeyd Boukhers

CONSTRAINT @ AAAI2021 1-8 2021
Baris, I., & Boukhers, Z. (2021). Early detection of COVID lies without evidences and propagation information. CONSTRAINT @ AAAI2021, 1–8.

Baris, Ipek, , and Zeyd Boukhers. "Early detection of COVID lies without evidences and propagation information." CONSTRAINT @ AAAI2021, 2021, pp. 1-8.

Baris, Ipek, , and Zeyd Boukhers. "Early detection of COVID lies without evidences and propagation information." CONSTRAINT @ AAAI2021 (2021): 1-8.
@inproceedings{proc16, title = {Early detection of COVID lies without evidences and propagation information}, author = {Ipek Baris and Zeyd Boukhers}, booktitle = {CONSTRAINT @ AAAI2021}, pages = {1-8}, year = {2021}, publisher = {ACM}, }
Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data

Zeyd Boukhers , Nagaraj Bahubali , Abinaya Thulsi Chandrasekaran , Adarsh Anand , Soniya Manchenahalli Gnanendra Prasad , Sriram Aralappa

BiblioDAP workshop @ SIGKDD2021 1-9 2021
Boukhers, Z., Bahubali, N., Chandrasekaran, A. T., Anand, A., Prasad, S. M. G., & Aralappa, S. (2021). Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data. BiblioDAP Workshop @ SIGKDD2021, 1–9.

Boukhers, Zeyd, , and Nagaraj Bahubali, , and Abinaya Thulsi Chandrasekaran, , and Adarsh Anand, , and Soniya Manchenahalli Gnanendra Prasad, , and Sriram Aralappa. "Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data." BiblioDAP workshop @ SIGKDD2021, 2021, pp. 1-9.

Boukhers, Zeyd, , and Nagaraj Bahubali, , and Abinaya Thulsi Chandrasekaran, , and Adarsh Anand, , and Soniya Manchenahalli Gnanendra Prasad, , and Sriram Aralappa. "Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data." BiblioDAP workshop @ SIGKDD2021 (2021): 1-9.
@inproceedings{proc17, title = {Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data}, author = {Zeyd Boukhers and Nagaraj Bahubali and Abinaya Thulsi Chandrasekaran and Adarsh Anand and Soniya Manchenahalli Gnanendra Prasad and Sriram Aralappa}, booktitle = {BiblioDAP workshop @ SIGKDD2021}, pages = {1-9}, year = {2021}, }

2016

Convoy detection in crowded surveillance videos

convoy detection surveillance video crowd analysis behavior understanding video analytics

Zeyd Boukhers , Yicong Wang , Kimiaki Shirahama , Kuniaki Uehara , Marcin Grzegorzek

Human Behavior Understanding (HBU) at ACM Multimedia International Workshop 2016 2016

PDF DOI
Boukhers, Z., Wang, Y., Shirahama, K., Uehara, K., & Grzegorzek, M. (2016, September). Convoy detection in crowded surveillance videos. Human Behavior Understanding (HBU) at ACM Multimedia International Workshop 2016. https://doi.org/10.1007/978-3-319-46843-3_9

Boukhers, Zeyd, , and Yicong Wang, , and Kimiaki Shirahama, , and Kuniaki Uehara, , and Marcin Grzegorzek. "Convoy detection in crowded surveillance videos." Human Behavior Understanding (HBU) at ACM Multimedia International Workshop 2016, 2016.

Boukhers, Zeyd, , and Yicong Wang, , and Kimiaki Shirahama, , and Kuniaki Uehara, , and Marcin Grzegorzek. "Convoy detection in crowded surveillance videos." Human Behavior Understanding (HBU) at ACM Multimedia International Workshop 2016 (2016).
@inproceedings{Boukhers2016Convoy, title = {Convoy detection in crowded surveillance videos}, author = {Zeyd Boukhers and Yicong Wang and Kimiaki Shirahama and Kuniaki Uehara and Marcin Grzegorzek}, booktitle = {Human Behavior Understanding (HBU) at ACM Multimedia International Workshop 2016}, year = {2016}, doi = {10.1007/978-3-319-46843-3_9}, url = {https://doi.org/10.1007/978-3-319-46843-3_9}, publisher = {Springer International Publishing}, }
This paper proposes detection of convoys in a crowded surveillance video. A convoy is defined as a group of pedestrians who are moving or standing together for a certain period of time. To detect such convoys, we firstly address pedestrian detection in a crowded scene, where small regions of pedestrians and their strong occlusions render usual object detection methods ineffective. Thus, we develop a method that detects pedestrian regions by clustering feature points based on their spatial characteristics. Then, positional transitions of pedestrian regions are analysed by our convoy detection method that consists of the clustering and intersection processes. The former finds groups of pedestrians in one frame by flexibly handling their relative spatial positions, and the latter refines groups into convoys by considering their temporal consistences over multiple frames. The experimental results on a challenging dataset shows the effectiveness of our convoy detection method.

2015

Object detection and depth estimation for 3D trajectory extraction

object detection depth estimation 3D trajectory video analysis multimedia indexing

Zeyd Boukhers , Kimiaki Shirahama , Frédéric Li , Marcin Grzegorzek

International Workshop on Content-Based Multimedia Indexing (CBMI 2015) 2015

PDF DOI
Boukhers, Z., Shirahama, K., Li, F., & Grzegorzek, M. (2015, July). Object detection and depth estimation for 3D trajectory extraction. International Workshop on Content-Based Multimedia Indexing (CBMI 2015). https://doi.org/10.1109/cbmi.2015.7153632

Boukhers, Zeyd, , and Kimiaki Shirahama, , and Frédéric Li, , and Marcin Grzegorzek. "Object detection and depth estimation for 3D trajectory extraction." International Workshop on Content-Based Multimedia Indexing (CBMI 2015), 2015.

Boukhers, Zeyd, , and Kimiaki Shirahama, , and Frédéric Li, , and Marcin Grzegorzek. "Object detection and depth estimation for 3D trajectory extraction." International Workshop on Content-Based Multimedia Indexing (CBMI 2015) (2015).
@inproceedings{Boukhers2015Object, title = {Object detection and depth estimation for 3D trajectory extraction}, author = {Zeyd Boukhers and Kimiaki Shirahama and Frédéric Li and Marcin Grzegorzek}, booktitle = {International Workshop on Content-Based Multimedia Indexing (CBMI 2015)}, year = {2015}, doi = {10.1109/cbmi.2015.7153632}, url = {https://doi.org/10.1109/cbmi.2015.7153632}, publisher = {IEEE}, }
To detect an event which is defined by the interaction of objects in a video, it is necessary to capture their spatio-temporal relation. However, the video only displays the original 3D space which is projected onto a 2D image plane. This paper introduces a method which extracts 3D trajectories of objects from 2D videos. Each trajectory represents the transition of an object’s positions in the 3D space. We extract such trajectories by combining object detection with depth estimation that estimates the depth information in 2D videos. The major problem for this is the inconsistency between object detection and depth estimation results. For example, significantly different depths may be estimated for the region of the same object, and an object region that is appropriately shaped by estimated depths may be missed. To overcome this, we first initialise the 3D position of an object by selecting the frame with the highest consistency between the object detection and depth estimation results. Then, we track the object in the 3D space using particle filter, where the 3D position of this object is modelled as a hidden state to generate its 2D visual appearance. Experimental results demonstrate the effectiveness of our method.

Books & Book Chapters

2017

3D Trajectory Extraction from 2D Videos for Human Activity Analysis

Zeyd Boukhers

44 2017
Boukhers, Z. (2017). 3D Trajectory Extraction from 2D Videos for Human Activity Analysis (Vol. 44). Logos Verlag Berlin GmbH.

Boukhers, Zeyd. "3D Trajectory Extraction from 2D Videos for Human Activity Analysis." , vol. 44, 2017.

Boukhers, Zeyd. "3D Trajectory Extraction from 2D Videos for Human Activity Analysis." 44 (2017).
@book{book1, title = {3D Trajectory Extraction from 2D Videos for Human Activity Analysis}, author = {Zeyd Boukhers}, volume = {44}, year = {2017}, publisher = {Logos Verlag Berlin GmbH}, }

Preprints & Technical Reports

2025

InstaDA: Augmenting Instance Segmentation Data with Dual-Agent System

Xianbao Hou , Yonghao He , Zeyd Boukhers , John See , Hu Su , Wei Sui , Cong Yang

arXiv preprint arXiv:2509.02973 2025

PDF DOI
Hou, X., He, Y., Boukhers, Z., See, J., Su, H., Sui, W., & Yang, C. (2025). InstaDA: Augmenting Instance Segmentation Data with Dual-Agent System. ArXiv Preprint ArXiv:2509.02973. https://doi.org/10.48550/arXiv.2509.02973

Hou, Xianbao, , and Yonghao He, , and Zeyd Boukhers, , and John See, , and Hu Su, , and Wei Sui, , and Cong Yang. "InstaDA: Augmenting Instance Segmentation Data with Dual-Agent System." arXiv preprint arXiv:2509.02973, 2025.

Hou, Xianbao, , and Yonghao He, , and Zeyd Boukhers, , and John See, , and Hu Su, , and Wei Sui, , and Cong Yang. "InstaDA: Augmenting Instance Segmentation Data with Dual-Agent System." arXiv preprint arXiv:2509.02973 (2025).
@article{hou2025instada, title = {InstaDA: Augmenting Instance Segmentation Data with Dual-Agent System}, author = {Xianbao Hou and Yonghao He and Zeyd Boukhers and John See and Hu Su and Wei Sui and Cong Yang}, journal = {arXiv preprint arXiv:2509.02973}, year = {2025}, doi = {10.48550/arXiv.2509.02973}, url = {https://arxiv.org/pdf/2509.02973}, }
Moir\backslash’eNet: A Compact Dual-Domain Network for Image Demoir\backslash’eing

Shuwei Guo , Simin Luan , Yan Ke , Zeyd Boukhers , John See , Cong Yang

arXiv preprint arXiv:2509.18910 2025

PDF DOI
Guo, S., Luan, S., Ke, Y., Boukhers, Z., See, J., & Yang, C. (2025). Moir\backslash’eNet: A Compact Dual-Domain Network for Image Demoir\backslash’eing. ArXiv Preprint ArXiv:2509.18910. https://doi.org/10.48550/arXiv.2509.18910

Guo, Shuwei, , and Simin Luan, , and Yan Ke, , and Zeyd Boukhers, , and John See, , and Cong Yang. "Moir\backslash’eNet: A Compact Dual-Domain Network for Image Demoir\backslash’eing." arXiv preprint arXiv:2509.18910, 2025.

Guo, Shuwei, , and Simin Luan, , and Yan Ke, , and Zeyd Boukhers, , and John See, , and Cong Yang. "Moir\backslash’eNet: A Compact Dual-Domain Network for Image Demoir\backslash’eing." arXiv preprint arXiv:2509.18910 (2025).
@article{guo2025moir, title = {Moir\backslash’eNet: A Compact Dual-Domain Network for Image Demoir\backslash’eing}, author = {Shuwei Guo and Simin Luan and Yan Ke and Zeyd Boukhers and John See and Cong Yang}, journal = {arXiv preprint arXiv:2509.18910}, year = {2025}, doi = {10.48550/arXiv.2509.18910}, url = {https://arxiv.org/pdf/2509.18910}, }
Reinforcement Learning for Large Language Model Fine-Tuning: A Systematic Literature Review

Lingxiao Kong , Qusai Ramdan , Oussama Zoubia , Jahid Hasan Polash , Mayra Elwes , Mehdi Akbari Gurabi , Lu Jin , Ekaterina Kutafina , Roman Matzutt , Yuanbin Wang , others

2025

PDF DOI
Kong, L., Ramdan, Q., Zoubia, O., Polash, J. H., Elwes, M., Gurabi, M. A., Jin, L., Kutafina, E., Matzutt, R., Wang, Y., & others. (2025). Reinforcement Learning for Large Language Model Fine-Tuning: A Systematic Literature Review. https://doi.org/10.21203/rs.3.rs-8196796/v1

Kong, Lingxiao, , and Qusai Ramdan, , and Oussama Zoubia, , and Jahid Hasan Polash, , and Mayra Elwes, , and Mehdi Akbari Gurabi, , and Lu Jin, , and Ekaterina Kutafina, , and Roman Matzutt, , and Yuanbin Wang, , and others. "Reinforcement Learning for Large Language Model Fine-Tuning: A Systematic Literature Review." , 2025.

Kong, Lingxiao, , and Qusai Ramdan, , and Oussama Zoubia, , and Jahid Hasan Polash, , and Mayra Elwes, , and Mehdi Akbari Gurabi, , and Lu Jin, , and Ekaterina Kutafina, , and Roman Matzutt, , and Yuanbin Wang, , and others. "Reinforcement Learning for Large Language Model Fine-Tuning: A Systematic Literature Review." (2025).
@article{kong2025reinforcement, title = {Reinforcement Learning for Large Language Model Fine-Tuning: A Systematic Literature Review}, author = {Lingxiao Kong and Qusai Ramdan and Oussama Zoubia and Jahid Hasan Polash and Mayra Elwes and Mehdi Akbari Gurabi and Lu Jin and Ekaterina Kutafina and Roman Matzutt and Yuanbin Wang and others}, year = {2025}, doi = {10.21203/rs.3.rs-8196796/v1}, url = {https://assets-eu.researchsquare.com/files/rs-8196796/v1_covered_96aef1fc-9b91-4ac6-8e6d-0c088657015f.pdf}, }

2021

Hybrid physics and deep learning model for interpretable vehicle state prediction

Alexandra Baier , Zeyd Boukhers , Steffen Staab

arXiv preprint arXiv:2103.06727 2021

PDF DOI
Baier, A., Boukhers, Z., & Staab, S. (2021). Hybrid physics and deep learning model for interpretable vehicle state prediction. ArXiv Preprint ArXiv:2103.06727. https://doi.org/10.48550/arXiv.2103.06727

Baier, Alexandra, , and Zeyd Boukhers, , and Steffen Staab. "Hybrid physics and deep learning model for interpretable vehicle state prediction." arXiv preprint arXiv:2103.06727, 2021.

Baier, Alexandra, , and Zeyd Boukhers, , and Steffen Staab. "Hybrid physics and deep learning model for interpretable vehicle state prediction." arXiv preprint arXiv:2103.06727 (2021).
@article{baier2021hybrid, title = {Hybrid physics and deep learning model for interpretable vehicle state prediction}, author = {Alexandra Baier and Zeyd Boukhers and Steffen Staab}, journal = {arXiv preprint arXiv:2103.06727}, year = {2021}, doi = {10.48550/arXiv.2103.06727}, url = {https://arxiv.org/pdf/2103.06727}, }

Publications

Journal Articles

2025

Context-agnostic data visitation to exchange health data for scientific research: the example of PADME PHT

R4-SLAM: Toward Real-time, Robust, and Resource-Restricted Visual SLAM in Dynamic Environments

2024

Skeleton ground truth extraction: methodology, annotation tool and benchmarks

BladeView: Towards Automatic Wind Turbine Inspection with Unmanned Aerial Vehicle

A non-enhanced CT based deep learning diagnostic system for high-risk COVID-19 infection in lung cancer patients

2023

Knowledge guided multi-filter residual convolutional neural network for ICD coding from clinical text

Deep author name disambiguation using DBLP data

2022

COIN: Counterfactual Image Generation for VQA Interpretation

2020

Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy

2019

Fractal approach for determining the optimal number of topics in the field of topic modeling

2017

Example-based 3D trajectories of objects from 2D videos

Less Restrictive Camera Odometry from Monocular Camera

Conference Papers

2026

Unleashing Semantic and Geometric Priors for 3D Scene Completion

2025

ELMTEX: Fine-tuning large language models for structured clinical information extraction. A case study on clinical reports

Future-Proofing Heritage with ARGUS: A Multimodal Digital Twin Approach for Sustainable Preservation

Q-Chain: A Causal-Aware Framework for Structural and Educational Question Generation

A unified framework for cultural heritage data historicity and migration: The ARGUS approach

EMORL: Ensemble multi-objective reinforcement learning for efficient and flexible LLM fine-tuning

Exploring the limits of model compression in LLMs: a knowledge distillation study on QA tasks

FAIR Data Assessment Using LLMs: The Fair-Way

2024

YawnNet: A Visual-Centric Approach for Yawning Detection

Moiré pattern detection: Stability and efficiency with evaluated loss function

Transition in focus of prediction tasks for skeleton graph component detection with Transformer

Large language model in medical informatics: direct classification and enhanced text representations for automatic ICD coding

From Theory to Practice: Demonstrators of FAIR Data Spaces Across Different Sectors

FDO manager: minimum viable FAIR digital object implementation

2023

Data trading and monetization: challenges and open research directions

NFDI4DS Infrastructure and Services

Research knowledge graphs in NFDI4DS

FAIR Data Spaces - Building a Common Cloud-Based Data Space for Industry and Science by Linking Gaia-X and NFDI

Beyond trading data: the hidden influence of public awareness and interest on cryptocurrency volatility

Enhancing Reproducibility in Research through FAIR Digital Objects

PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences

Enhancing data space semantic interoperability through Machine Learning: a visionary perspective

2022

Whois? Deep author name disambiguation using bibliographic data

Vision and Natural Language for Metadata Extraction from PDF Documents: A Multimodal Approach

2021

MexPub: Deep transfer learning for metadata extraction from German publications

BiblioDAP’21: The 1st workshop on bibliographic data analysis and processing

2019

An end-to-end approach for extracting and segmenting high-variance references from PDF documents

EXCITE – A toolchain to extract, match and publish open literature references

2016

Automatic recognition of movement patterns in the Vojta therapy using RGB-D data

2015

Shape-Based Eye Blinking Detection and Analysis

Environmental microbiological content-based image retrieval system using internal structure histogram

Extracting 3D Trajectories of Objects from 2D Videos Using Particle Filter

Workshop Papers

2025

MESD: Metadata Extraction from Scholarly Documents - A Shared Task Overview

Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper

A survey on metadata for Machine Learning models and datasets: standards, practices, and harmonization challenges

Multi-Objective Reinforcement Learning for Large Language Model Optimization: Visionary Perspective

FAIR Data Publisher: Strategie für den flächendeckenden Einsatz von FAIR Digital Objects

2024

Falcon 7b for software mention detection in scholarly documents

2023

Pedestrian collision prediction using a monocular camera

2022

Evaluation scheme of FAIRness in scholarly data

NFDI4DataScience Registry for Reproducible Data Science and Artificial Intelligence

2021

LaHAR: Latent Human Activity Recognition using LDA

Early detection of COVID lies without evidences and propagation information