@inproceedings{overdevest_deep_2024,

title = {Deep Unfolding for Sparse Distance Recovery in PMCW MIMO Automotive Radar: 21st European Radar Conference, EuRAD 2024},

author = {J. Overdevest and J. Ji and A. G. C. Koppelaar and A. Pandharipande and H. J. W. Belt and R. J. G. Van Sloun},

url = {https://www.scopus.com/pages/publications/85210804447},

doi = {10.23919/EuRAD61604.2024.10734898},

year  = {2024},

date = {2024-11-01},

urldate = {2024-11-01},

booktitle = {2024 21st European Radar Conference, EuRAD 2024},

pages = {31–34},

abstract = {Phase-Modulated Continuous Wave (PMCW) radars have attracted significant attention due to advances in mm-wave technology, waveform design, and digital signal processing. The de facto technique for distance estimation in PMCW radar receivers is matched filtering with a bank of correlators. There is an inherent trade-off between support for multiple antennas (MIMO support), sequences with good correlation properties and the maximum achievable unambiguous range/velocity. An important challenge in PMCW MIMO radars is to design receivers that result in low sidelobe levels in the range domain, for a given choice of sequences. In this paper, we propose a novel range processing scheme by formulating an optimization problem with l1-norm regularization that promotes sparse distance estimates. To solve this, we propose deep unfolded FISTA and ADMM algorithms for distance sidelobe suppression and restoration of the orthogonality of the transmitted codewords. We show that the proposed method achieves better dynamic range compared to the traditional matched filtering approach.},

note = {Publisher: Institute of Electrical and Electronics Engineers},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wei_score-based_2024,

title = {Score-based Generative Modeling for Interference Mitigation in Automotive FMCW Radar: 21st European Radar Conference, EuRAD 2024},

author = {X. Wei and J. Overdevest and J. Li and J. Youn and S. Ravindran and R. J. G. Van Sloun},

url = {https://www.scopus.com/pages/publications/85210853997},

doi = {10.23919/EuRAD61604.2024.10734954},

year  = {2024},

date = {2024-11-01},

urldate = {2024-11-01},

booktitle = {2024 21st European Radar Conference, EuRAD 2024},

pages = {27–30},

abstract = {Automotive radar interference is a growing problem as automotive radars proliferate in advanced driver assistance systems and autonomous driving. Numerous studies have been proposed to address interference mitigation based on hand-crafted priors, like sparsity-based techniques, or through purely data-driven approaches. However, their effectiveness is often compromised when these representations fail to accurately reflect the statistical characteristics of the interfering radar parameters in dynamic scenarios. In this work, we propose a new method that treats interference mitigation as a source separation problem. We leverage score-based generative networks to explicitly learn the interfering radar parameters. These learned parameters are subsequently combined with Maximum-A-posteriori estimation, allowing for an algorithm with enhanced performance. We demonstrate that our algorithm outperforms the baselines in signal-To-noise ratio.},

note = {Publisher: Institute of Electrical and Electronics Engineers},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{youn_interference_2024,

title = {Interference Mitigation Evaluation Methodology for Automotive Radar},

author = {J. Youn and J. Li and R. Wu and J. Overdevest},

url = {https://ieeexplore.ieee.org/document/10734960},

doi = {10.23919/EuRAD61604.2024.10734960},

year  = {2024},

date = {2024-09-01},

urldate = {2024-09-01},

booktitle = {2024 21st European Radar Conference (EuRAD)},

pages = {115–118},

abstract = {Interference mitigation is crucial to restore the degraded detection performance of interfered radars. For developing advanced interference mitigation methods, a well-defined evaluation methodology is required to assess the performance of different methods thoroughly and appropriately. In this paper, we propose to evaluate interference mitigation methods by measuring detection performance and signal-to-noise ratio in the range-Doppler domain. We especially suggest measuring the performance analytically without involving any detector but based on the estimated probability density functions of the target and background to isolate the effect of the detector in the performance evaluation. The time-domain thresholding and time-frequency domain thresholding methods are compared to validate the proposed methodology on simulated data.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{de_oliveira_fusion_nodate,

title = {Fusion Model Using a Neural Network and MLE for a Single Snapshot DOA Estimation with Imperfection Mitigation textbar Request PDF},

author = {M. L. L. De Oliveira and M. J. G. Bekooij},

url = {https://www.researchgate.net/publication/376925649_Fusion_Model_Using_a_Neural_Network_and_MLE_for_a_Single_Snapshot_DOA_Estimation_with_Imperfection_Mitigation},

doi = {10.1109/RADAR54928.2023.10371066},

year  = {2023},

date = {2023-10-01},

urldate = {2023-10-01},

booktitle = {ResearchGate},

abstract = {Request PDF textbar On Nov 6, 2023, Marcio L. Lima De Oliveira and others published Fusion Model Using a Neural Network and MLE for a Single Snapshot DOA Estimation with Imperfection Mitigation textbar Find, read and cite all the research you need on ResearchGate},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{overdevest_signal_2023,

title = {Signal Reconstruction for FMCW Radar Interference Mitigation Using Deep Unfolding: 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023},

author = {J. Overdevest and A. G. C. Koppelaar and M. J. G. Bekooij and J. Youn and R. J. G. Sloun},

url = {https://www.scopus.com/pages/publications/86000372404},

doi = {10.1109/ICASSP49357.2023.10096297},

year  = {2023},

date = {2023-05-01},

urldate = {2023-05-01},

booktitle = {ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

abstract = {Removal of frequency-modulated continuous wave (FMCW) interference by zeroing corrupted samples causes significant distortions and peak power losses in the range-Doppler map. Existing methods aim to diminish these distortions by utilizing data from one dimension to reconstruct the corrupted samples, which do not perform well when a large number of samples are interfered and have difficulty recovering weak target signals.In this paper, model-based deep learning interference mitigation algorithms, called ALISTA and ALFISTA, are presented that reduce these artifacts by leveraging the full integration gain using all uncorrupted fast-time and slow-time samples. Simulations with 50% corrupted samples show that target peak power loss and velocity peak-to-sidelobe ratio (VPSR) with a 20-layer ALFISTA improves with 5.5 and 9.6 dB compared to zeroing. Furthermore, significant improvements in precision and recall are observed, even when large amounts (50-80%) of samples are missing.},

note = {Publisher: Institute of Electrical and Electronics Engineers},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{du2023protodiff,

title = {ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion},

author = {Y. Du and Z. Xiao and S. Liao and C. G. M. Snoek},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{shen2023episodic,

title = {Episodic Multi-Task Learning with Heterogeneous Neural Processes},

author = {J. Shen and X. Zhen and C. Wang and M. Worring},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{derakhshani2023bayesian,

title = {Bayesian Prompt Learning for Image-Language Model Generalization},

author = {M. M. Derakhshani and E. Sanchez and A. Bulat and V. G. T. Costa and C. G. M. Snoek and G. Tzimiropoulos and B. Martinez},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {IEEE/CVF International Conference on Computer Vision (ICCV)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vansonsbeek2023openended,

title = {Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models},

author = {T. Sonsbeek and M. Mahdi Derakhshani and I. Najdenkoska and C. G. M. Snoek and M. Worring},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{du2023emo,

title = {EMO: Episodic Memory Optimization for Few-Shot Meta-Learning},

author = {Y. Du and J. Shen and X. Zhen and C. G. M. Snoek},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Conference on Lifelong Learning Agents (CoLLAs)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sun2023metamodulation,

title = {MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks},

author = {W. Sun and Y. Du and X. Zhen and F. Wang and L. Wang and C. G. M. Snoek},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {International Conference on Machine Learning (ICML)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{du2023superdisco,

title = {SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail},

author = {Y. Du and J. Shen and X. Zhen and C. G. M. Snoek},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{najdenkoska2023meta,

title = {Meta Learning To Bridge Vision and Language Models for Multimodal Few-Shot Learning},

author = {I. Najdenkoska and X. Zhen and M. Worring},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {International Conference on Learning Representations (ICLR)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{xiao2023energy,

title = {Energy-Based Test Sample Adaptation for Domain Generalization},

author = {Z. Xiao and X. Zhen and S. Liao and C. G. M. Snoek},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {International Conference on Learning Representations (ICLR)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vansonsbeek2023probabilistic,

title = {Probabilistic Integration of Object Level Annotations in Chest X-Ray Classification},

author = {T. Sonsbeek and X. Zhen and D. Mahapatra and M. Worring},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{shen2022association,

title = {Association Graph Learning for Multi-Task Classification with Category Shifts},

author = {J. Shen and Z. Xiao and X. Zhen and C. G. M. Snoek and M. Worring},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{derakhshani2022lifelonger,

title = {LifeLonger: A Benchmark for Continual Disease Classification},

author = {M. M. Derakhshani and I. Najdenkoska and T. Sonsbeek and X. Zhen and D. Mahapatra and M. Worring and C. G. M. Snoek},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zhang2022audio,

title = {Audio-Adaptive Activity Recognition Across Video Domains},

author = {Y. Zhang and H. Doughty and X. Zhen and C. G. M. Snoek},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{du2022hierarchical,

title = {Hierarchical Variational Memory for Few-Shot Learning Across Domains},

author = {Y. Du and X. Zhen and L. Shao and C. G. M. Snoek},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {International Conference on Learning Representations (ICLR)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{xiao2022learning,

title = {Learning to Generalize Across Domains on Single Test Samples},

author = {Z. Xiao and X. Zhen and L. Shao and C. G. M. Snoek},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {International Conference on Learning Representations (ICLR)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{du2021metanorm,

title = {MetaNorm: Learning to Normalize Few-Shot Batches Across Domains},

author = {Y. Du and X. Zhen and L. Shao and C. G. M. Snoek},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {International Conference on Learning Representations (ICLR)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{shen2021variational,

title = {Variational Multi-Task Learning with Gumbel-Softmax Priors},

author = {J. Shen and X. Zhen and M. Worring and L. Shao},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vansonsbeek2021variational,

title = {Variational Knowledge Distillation for Disease Classification in Chest X-Rays},

author = {T. Sonsbeek and X. Zhen and M. Worring and L. Shao},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Information Processing in Medical Imaging (IPMI)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{najdenkoska2021variational,

title = {Variational Topic Inference for Chest X-Ray Report Generation},

author = {I. Najdenkoska and X. Zhen and M. Worring and L. Shao},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{du2021metalearning,

title = {Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation},

author = {Y. Du and N. Holla and X. Zhen and C. G. M. Snoek and E. Shutova},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Findings of the Association for Computational Linguistics (ACL)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{derakhshani2021kernel,

title = {Kernel Continual Learning},

author = {M. M. Derakhshani and X. Zhen and L. Shao and C. G. M. Snoek},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {International Conference on Machine Learning (ICML)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{xiao2021bit,

title = {A Bit More Bayesian: Domain-Invariant Learning with Uncertainty},

author = {Z. Xiao and J. Shen and X. Zhen and L. Shao and C. G. M. Snoek},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {International Conference on Machine Learning (ICML)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wang2021variational,

title = {Variational Prototype Inference for Few-Shot Semantic Segmentation},

author = {H. Wang and Y. Yang and X. Cao and X. Zhen and C. G. M. Snoek and L. Shao},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wang2021nformer,

title = {NFormer: Robust Person Re-identification with Neighbor Transformer},

author = {H. Wang and J. Shen and Y. Liu and Y. Gao and E. Gavves},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bartels2021learning,

title = {Learning to Automatically Generate Accurate ECG Captions},

author = {M. G. G. Bartels and I. Najdenkoska and R. Leur and A. Sammani and K. Taha and D. M. Knigge and P. A. Doevendans and M. Worring and R. Es},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Medical Imaging with Deep Learning (MIDL)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zhen2020learning,

title = {Learning to Learn Kernels with Variational Random Features},

author = {X. Zhen and Y. Du and H. Sun and J. Xu and Y. Yin and L. Shao and C. G. M. Snoek},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {International Conference on Machine Learning (ICML)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zhen2020variational,

title = {Learning to Learn Variational Semantic Memory},

author = {X. Zhen and Y. Du and H. Xu and L. Shao and C. G. M. Snoek},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{du2020learning,

title = {Learning to Learn with Variational Information Bottleneck for Domain Generalization},

author = {Y. Du and X. Zhen and J. Xu and H. Xiong and Q. Qiu and L. Shao and C. G. M. Snoek},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {European Conference on Computer Vision (ECCV)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{du2020variational,

title = {Variational Image Deraining},

author = {Y. Du and J. Xu and Q. Qiu and X. Zhen and L. Zhang},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sergidou_probing_2026,

title = {Probing Content and Channel in Speaker Verification Models},

author = {E. K. Sergidou and D. Bikker and C. Pouw and J. Rohdin and Z. Geradts and M. Worring},

year  = {2026},

date = {2026-02-12},

booktitle = {ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jilani_decoding_2023,

title = {Decoding deception: Understanding human discrimination ability in differentiating authentic faces from deepfake deceits},

author = {S. K. Jilani and Z. Geradts and A. Abubakar},

year  = {2023},

date = {2023-11-30},

booktitle = {International Conference on Image Analysis and Processing},

pages = {470–481},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez_improved_2023,

title = {Improved likelihood ratios for surveillance video face recognition with multimodal feature pairing},

author = {A. M. Rodríguez and Z. Geradts and M. Worring and L. Unzueta},

year  = {2023},

date = {2023-02-23},

booktitle = {2023 11th International Workshop on Biometrics and Forensics (IWBF)},

pages = {1–6},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kula2024context,

title = {Context-Aware Automated Sprint Plan Generation for Agile Software Development},

author = {E. Kula and A. Deursen and G. Gousios},

doi = {10.1145/3691620.3695540},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering},

note = {ACM SIGSOFT Distinguished Paper Award},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{siachamis2024evaluating,

title = {Evaluating stream processing autoscalers},

author = {G. Siachamis and G. Christodoulou and K. Psarakis and M. Fragkoulis and A. Deursen and A. Katsifodimos},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 18th ACM International Conference on Distributed and Event-based Systems},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kapel2024difficulty,

title = {On the Difficulty of Identifying Incident-Inducing Changes},

author = {E. Kapel and L. Cruz and D. Spinellis and A. Deursen},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{poenaru2024anomaly,

title = {Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World},

author = {L. Poenaru-Olaru and N. Karpova and L. Cruz and J. S. Rellermeyer and A. Deursen},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering-Software Engineering for AI},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{siachamis2024checkmate,

title = {CheckMate: Evaluating Checkpointing Protocols for Streaming Dataflows},

author = {G. Siachamis and K. Psarakis and M. Fragkoulis and A. Deursen and P. Carbone and A. Katsifodimos},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {IEEE 40th International Conference on Data Engineering (ICDE)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kapel2024enhancing,

title = {Enhancing Incident Management: Insights from a Case Study at ING},

author = {E. Kapel and L. Cruz and D. Spinellis and A. Deursen},

doi = {10.1145/3643665.3648048},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 1st IEEE/ACM Workshop on Software Engineering Challenges in Financial Firms (FinanSE)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{salimzadeh2024decisiontime,

title = {“DecisionTime”: A Configurable Framework for Reproducible Human-AI Decision-Making Studies},

author = {S. Salimzadeh and U. Gadiraju},

doi = {10.1145/3631700.3664885},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{salimzadeh2024doubt,

title = {When in Doubt! Understanding the Role of Task Characteristics on Peer Decision-Making with AI Assistance},

author = {S. Salimzadeh and U. Gadiraju},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{salimzadeh2024uncertainty,

title = {Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making},

author = {S. Salimzadeh and G. He and U. Gadiraju},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the CHI Conference on Human Factors in Computing Systems},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{altmeyer2024faithful,

title = {Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals},

author = {P. Altmeyer and M. Farmanbar and A. Deursen and C. C. S. Liem},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{poenaru2023retrain,

title = {Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques},

author = {L. Poenaru-Olaru and J. Sallou and L. Cruz and J. S. Rellermeyer and A. Deursen},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {IEEE/ACM 7th International Workshop on Green And Sustainable Software, GREENS 2023},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{poenaru2023maintaining,

title = {Maintaining and Monitoring AIOps Models Against Concept Drift},

author = {L. Poenaru-Olaru and L. Cruz and J. S. Rellermeyer and A. Deursen},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kapel2023incident,

title = {Incident Prevention Through Reliable Changes Deployment},

author = {E. Kapel},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{altmeyer2023endogenous,

title = {Endogenous Macrodynamics in Algorithmic Recourse},

author = {P. Altmeyer and G. Angela and A. Buszydlik and K. Dobiczek and A. Deursen and C. C. S. Liem},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{salimzadeh2023missing,

title = {A Missing Piece in the Puzzle: Considering the Role of Task Complexity in Human-AI Decision Making},

author = {S. Salimzadeh and G. He and U. Gadiraju},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{siachamis2023evaluating,

title = {Towards Evaluating Stream Processing Autoscalers},

author = {G. Siachamis and J. Kanis and W. Koper and K. Psarakis and M. Fragkoulis and A. Deursen and A. Katsifodimos},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{siachamis2023adaptive,

title = {Adaptive distributed streaming similarity joins},

author = {G. Siachamis and K. Psarakis and M. Fragkoulis and O. Papapetrou and A. Deursen and A. Katsifodimos},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Proceedings of the 17th ACM International Conference on Distributed and Event-based Systems},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{petrescu2023log,

title = {Log parsing evaluation in the era of modern software systems},

author = {S. Petrescu and F. Den Hengst and A. Uta and J. S. Rellermeyer},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{applis2023searching,

title = {Searching for Quality: Genetic Algorithms and Metamorphic Testing for Software Engineering ML},

author = {L. Applis and A. Panichella and R. Marang},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{applis2023hasbugs,

title = {HasBugs-Handpicked Haskell Bugs},

author = {L. Applis and A. Panichella},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kula2023dynamic,

title = {Dynamic Prediction of Delays in Software Projects Using Delay Patterns and Bayesian Modeling},

author = {E. Kula and E. Greuter and A. Deursen and G. Gousios},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Proceedings of the ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{poenaru2022concept,

title = {Are Concept Drift Detectors Reliable Alarming Systems? A Comparative Study},

author = {L. Poenaru-Olaru and L. Cruz and A. Deursen and J. S. Rellermeyer},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {IEEE International Conference on Big Data},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{chuang2022removing,

title = {Removing dependencies from large software projects: are you really sure?},

author = {C. C. Chuang and L. Cruz and R. Van Dalen and V. Mikovski and A. Deursen},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zhang2022code,

title = {Code smells for machine learning applications},

author = {H. Zhang and L. Cruz and A. Deursen},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Proceedings of the 1st international conference on AI engineering: software engineering for AI (CAIN)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{altmeyer2022explaining,

title = {Explaining Black-Box Models through Counterfactuals},

author = {P. Altmeyer and A. Deursen and C. C. S. Liem},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Proceedings of JuliaCon},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{denhengst2022reinforcement,

title = {Reinforcement Learning with Option Machines},

author = {F. Hengst and V. François-Lavet and M. Hoogendoorn and F. Harmelen},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {International Joint Conference on Artificial Intelligence},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vanoort2022experiences,

title = {Experiences in Analysing the Software Quality of ML Projects with mllint},

author = {B. Oort and L. Cruz and B. Loni and A. Deursen},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {44th International Conference on Software Engineering (ICSE 2022), Software Engineering in Practice (SEIP)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{siachamis2021integrating,

title = {Integrating Massive Data Streams},

author = {G. Siachamis and G. J. P. M. Houben and A. Deursen and A. Katsifodimos},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Proceedings of the VLDB 2021 PhD Workshop},

volume = {2971},

series = {CEUR Workshop Proceedings},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{salimzadeh2021impact,

title = {The Impact of Entity Cards on Learning-Oriented Search Tasks},

author = {S. Salimzadeh and D. Maxwell and C. Hauff},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Proceedings of the ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR)},

note = {Honorable Mention for Best Student Paper; Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kula2021modeling,

title = {Modeling Team Dynamics for the Characterization and Prediction of Delays in User Stories},

author = {E. Kula and A. Deursen and G. Gousios},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Proceedings of the IEEE/ACM International Conference on Automated Software Engineering},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vanderleij2021data,

title = {Data-Driven Extract Method Recommendations: A Study at ING},

author = {D. Leij and J. Binda and R. Dalen and P. Vallen and Y. Luo and M. Aniche},

doi = {10.1145/3468264.3473927},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{koutras2021valentine,

title = {Valentine: Evaluating Matching Techniques for Dataset Discovery},

author = {C. Koutras and G. Siachamis and A. Ionescu and K. Psarakis and J. Brons and M. Fragkoulis and C. Lofi and A. Bonifati and A. Katsifodimos},

doi = {10.1109/ICDE51399.2021.00047},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {37th IEEE International Conference on Data Engineering (ICDE)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vanoort2021prevalence,

title = {The Prevalence of Code Smells in Machine Learning projects},

author = {B. Oort and L. Cruz and M. Aniche and A. Deursen},

doi = {10.1109/WAIN52551.2021.00011},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {WAIN’21 - 1st Workshop on AI Engineering – Software Engineering for AI},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{xie2021systematic,

title = {Systematic Mapping Study on the Machine Learning Lifecycle},

author = {Y. Xie and L. Cruz and P. Heck and J. S. Rellermeyer},

doi = {10.1109/WAIN52551.2021.00017},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {WAIN’21 - 1st Workshop on AI Engineering – Software Engineering for AI},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{applis2021assessing,

title = {Assessing Robustness of ML-Based Program Analysis Tools using Metamorphic Program Transformations},

author = {L. Applis and A. Panichella and A. Deursen},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Proceedings of the IEEE/ACM International Conference on Automated Software Engineering (ASE)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{huijgens2020questions,

title = {Questions for Data Scientists in Software Engineering: A Replication},

author = {H. Huijgens and A. Rastogi and E. Mulders and G. Gousios and A. Deursen},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/SIGSOFT FSE)},

pages = {568–579},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{denhengst2019reinforcement,

title = {Reinforcement Learning for Personalized Dialogue Management},

author = {F. Hengst and M. Hoogendoorn and F. Harmelen and J. Bosman},

doi = {10.1145/3350546.3352501},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {IEEE/WIC/ACM International Conference on Web Intelligence (WI 2019)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kula2019releasing,

title = {Releasing Fast and Slow: An Exploratory Case Study at ING},

author = {E. Kula and A. Rastogi and H. Huijgens and A. Deursen and G. Gousios},

doi = {10.1145/3338906.3338978},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{huijgens2019factors,

title = {Factors Affecting Cloud Infra-Service Development Lead Times: A Case Study at ING},

author = {H. Huijgens and E. Greuter and J. Brons and E. A. Doorn and I. Papadopoulos and F. M. Martinez and M. Aniche and O. Visser and A. Deursen},

doi = {10.1145/3338906.3338978},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {Proceedings of the ACM/IEEE International Conference on Software Engineering (ICSE): Software Engineering in Practice (SEIP)},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{huijgens2018software,

title = {Software Analytics in Continuous Delivery: A Case Study on Success Factors},

author = {H. Huijgens and D. Spadini and D. Stevens and N. Visser and A. Deursen},

doi = {10.1145/3239235.3240505},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {ESEM ’18: Proceedings of the 12th International Symposium on Empirical Software Engineering and Measurement},

note = {Preprint},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{basar2025well,

title = {How well can large language models reflect? A human evaluation of LLM-generated reflections for motivational interviewing dialogues},

author = {E. Basar and X. Sun and I. Hendrickx and J. Wit and T. Bosse and G. J. de Bruijn and J. A. Bosch and E. Krahmer},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the 31st international conference on computational linguistics},

pages = {1964–1982},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{basar2024extent,

title = {To what extent are large language models capable of generating substantial reflections for motivational interviewing counseling chatbots? A human evaluation},

author = {E. Basar and I. Hendrickx and E. Krahmer and G. J. Bruijn and T. Bosse},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 1st Human-Centered Large Language Modeling Workshop},

pages = {41–52},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{he2024exploring,

title = {Exploring user engagement through an interaction lens: what textual cues can tell us about human-chatbot interactions},

author = {L. He and A. Braggaar and E. Basar and E. Krahmer and M. Antheunis and R. Wiers},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 6th ACM Conference on Conversational User Interfaces},

pages = {1–14},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{basar2023hyleca,

title = {HyLECA: a framework for developing hybrid long-term engaging controlled conversational agents},

author = {E. Basar and D. Balaji and L. He and I. Hendrickx and E. Krahmer and G. J. Bruijn and T. Bosse},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Proceedings of the 5th international conference on conversational user interfaces},

pages = {1–5},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bacsar2022hints,

title = {Hints of independence in a pre-scripted world: On controlled usage of open-domain language models for chatbots in highly sensitive domains},

author = {E. Basar and I. Hendrickx and E. Krahmer and G. J. Bruijn and T. Bosse},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Proceedings of the 14th International Conference on Agents and Artificial Intelligence},

pages = {401–407},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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title = {Learning From Scenarios for Stochastic Repairable Scheduling},

author = {K. van den Houten and D. M. J. Tax and E. Freydell and M. Weerdt},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR)},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{heidergott2022analysis,

title = {Analysis of Measure-Valued Derivatives in a Reinforcement Learning Actor-Critic Framework},

author = {B. Heidergott and E. Krieken and K. van den Houten},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

publisher = {Association for Computing Machinery},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sergidou_probing_2026,

title = {Probing Content and Channel in Speaker Verification Models},

author = {E. K. Sergidou and D. Bikker and C. Pouw and J. Rohdin and Z. Geradts and M. Worring},

year  = {2026},

date = {2026-02-12},

booktitle = {ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jilani_decoding_2023,

title = {Decoding deception: Understanding human discrimination ability in differentiating authentic faces from deepfake deceits},

author = {S. K. Jilani and Z. Geradts and A. Abubakar},

year  = {2023},

date = {2023-11-30},

booktitle = {International Conference on Image Analysis and Processing},

pages = {470–481},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez_improved_2023,

title = {Improved likelihood ratios for surveillance video face recognition with multimodal feature pairing},

author = {A. M. Rodríguez and Z. Geradts and M. Worring and L. Unzueta},

year  = {2023},

date = {2023-02-23},

booktitle = {2023 11th International Workshop on Biometrics and Forensics (IWBF)},

pages = {1–6},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kuo2026developer,

title = {Developer Interaction Patterns with Proactive AI: A Five-Day Field Study},

author = {N. Kuo and A. Sergeyuk and V. Chen and M. Izadi},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

booktitle = {Proceedings of the Annual ACM Conference on Intelligent User Interfaces (IUI)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{koohestani2025agentguard,

title = {AgentGuard: Runtime Verification of AI Agents},

author = {R. Koohestani},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the First International Workshop on Autonomous Agents in Software Engineering (AgenticSE Workshop)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{li2025prompt,

title = {Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering},

author = {Z. Li and A. Sergeyuk and M. Izadi},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE 2025 - Industry Track)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sergeyuk2025developer,

title = {Developer Needs and Feasible Features for AI Assistants in IDEs},

author = {A. Sergeyuk and E. Koshchenko and I. Zakharov and T. Bryksin and M. Izadi},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the IEEE/ACM International Conference on Software Engineering (ICSE 2026 - SEIP)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cipollone2025treeranker,

title = {TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions in IDEs},

author = {D. Cipollone and E. Bogomolov and A. Deursen and M. Izadi},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE 2025 - Industry Track)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{noviello2025multimodal,

title = {Multimodal Analogy Generation in Programming Education},

author = {Y. Noviello and A. Birillo and G. Migut},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education Vol. 2 (ITiCSE 2025)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{noviello2025designing,

title = {Designing and Evaluating AI-Generated Multimodal Analogy-Based Explanations},

author = {Y. Noviello},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the 26th International Conference on Artificial Intelligence in Education (AIED 2025 - Doctoral Consortium)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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title = {Improving the Comprehensibility of Generated Test Suites Using Test Case Clustering},

author = {M. Olsthoorn},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the 18th IEEE International Conference on Software Testing, Verification and Validation (ICST 2025 - Short Papers, Vision and Emerging Results Track)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{abdullin2025testwars,

title = {Test Wars: A Comparative Study of SBST, Symbolic Execution, and LLM-Based Approaches to Unit Test Generation},

author = {A. Abdullin and P. Derakhshanfar and A. Panichella},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the 18th IEEE International Conference on Software Testing, Verification and Validation (ICST 2025 - Research Papers Track)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zakharov2025when,

title = {When People Come First, A Human-Centered Approach to Computer Science Education},

author = {I. Zakharov and L. Piatnitckaia and A. Birillo and A. Sergeyuk and M. Izadi},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE 2025 - Panel Contribution)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{li2025enhancing,

title = {Enhancing Human-IDE Interaction in the SDLC using LLM-based Mediator Agents},

author = {Z. Li and M. Izadi},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the 1st International Workshop on AI-Augmented SDLC (AI-SDLC Workshop)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{li2025mediating,

title = {Mediating between Human Programmers and Integrated Development Environments using LLM-based Agents},

author = {Z. Li},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the 2025 ACM International Conference on the Foundations of Software Engineering (FSE 2025 - Doctoral Symposium)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cipollone2025enhancing,

title = {Enhancing Large Language Model Integration in Integrated Development Environments},

author = {D. Cipollone},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the ACM International Conference on the Foundations of Software Engineering (FSE 2025 - Doctoral Symposium)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{koohestani2025hyperseq,

title = {HyperSeq: A Hyper-Adaptive Representation for Predictive Sequencing of States},

author = {R. Koohestani and M. Izadi},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the ACM International Conference on the Foundations of Software Engineering (FSE 2025 - Ideas, Visions and Reflections)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ionescu2025multiagent,

title = {A Multi-agent Onboarding Assistant based on Large Language Models, Retrieval Augmented Generation, and Chain-of-Thought},

author = {A. C. Ionescu and S. Titov and M. Izadi},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the ACM International Conference on the Foundations of Software Engineering (FSE 2025 - Demonstration)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{panichella2025metamorphic,

title = {Metamorphic-Based Many-Objective Distillation of LLMs for Code-related Tasks},

author = {A. Panichella},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the 47th IEEE/ACM International Conference on Software Engineering (ICSE 2025 - Research Track)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{koohestani2025rethinking,

title = {Rethinking IDE Customization for Enhanced HAX: A Hyperdimensional Perspective},

author = {R. Koohestani and M. Izadi},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the 2nd Workshop on Integrated Development Environments (the IDE Workshop)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{brockbernd2024understanding,

title = {Understanding Concurrency Bugs in Real-World Programs with Kotlin Coroutines},

author = {B. Brockbernd and N. Koval and A. Deursen and B. Kulahcioglu Ozkan},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 38th European Conference on Object-Oriented Programming (ECOOP 2024)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{demoor2024transformer,

title = {A Transformer-Based Approach for Smart Invocation of Automatic Code Completion},

author = {A. Moor and A. Deursen and M. Izadi},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 1st ACM International Conference on AI-Powered Software (AIWare 2024)},

note = {ACM Distinguished Paper Award},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sergeyuk2024inide,

title = {In-IDE Human-AI Experience in the Era of Large Language Models: A Literature Review},

author = {A. Sergeyuk and S. Titov and M. Izadi},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 1st Workshop on Integrated Development Environments (the IDE Workshop)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sapozhnikov2024testspark,

title = {TestSpark: IntelliJ IDEA's Ultimate Test Generation Companion},

author = {A. Sapozhnikov and M. Olsthoorn and A. Panichella and V. V. Kovalenko and P. Derakhshanfar},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 2024 ACM/IEEE 46th International Conference on Software Engineering (ICSE 2024)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{georgescu2024evolutionary,

title = {Evolutionary Generative Fuzzing for Differential Testing of the Kotlin Compiler},

author = {C. Georgescu and M. Olsthoorn and P. Derakhshanfar and M. Akhin and A. Panichella},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the ACM International Conference on the Foundations of Software Engineering (FSE 2024 - Industry Track)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jabarimani_detectability_2026,

title = {Detectability of white matter hyperintensities in 0.6T FLAIR scans},

author = {N. Jabarimani and J. Bresser and E. Ercan and M. Staring and M. Osch and M. Nagtegaal},

url = {http://echo.ismrm.org/abstracts/view/1bc089b6-b700-4c07-a5d7-0d372f19dc2e},

year  = {2026},

date = {2026-07-01},

publisher = {ISMRM},

address = {Cape Town, South Africa},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lyu_enhancement_2026,

title = {Enhancement of Mid-Field (0.6T) T2-Weighted Prostate Scans Using a Two-Stage Refinement Framework},

author = {D. Lyu and M. Nagtegaal and E. Ercan and Z. Li and M. WJ Straten and M. Staring and A. Webb and M. Osch and P. Börnert and Y. Dong},

url = {http://echo.ismrm.org/abstracts/view/8cc6fddd-0042-46ad-8c1e-3734460f2009},

year  = {2026},

date = {2026-07-01},

booktitle = {Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition},

publisher = {ISMRM},

address = {Cape Town, South Africa},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lyu_crosscascade_2026,

title = {Cross-Cascade Feature Aggregation for Improved Spatio-Temporal Reconstruction in Cardiac Cine MRI},

author = {D. Lyu and M. Staring and Y. Dong and J. Keupp and H. Lamb and M. Doneva},

url = {http://echo.ismrm.org/abstracts/view/6e76040f-ed27-486b-a6fa-02e8ec8c1741},

year  = {2026},

date = {2026-07-01},

booktitle = {Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition},

publisher = {ISMRM},

address = {Cape Town, South Africa},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lyu_kpinr_2026,

title = {KP-INR: A Dual-Branch Implicit Neural Representation Model for Cardiac Cine MRI Reconstruction},

author = {D. Lyu and M. Staring and M. Doneva and H. J. Lamb and N. Pezzotti},

doi = {10.1007/978-3-032-17734-6_6},

isbn = {978-3-032-17734-6},

year  = {2026},

date = {2026-07-01},

urldate = {2026-07-01},

booktitle = {Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers},

pages = {56–66},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Cardiac Magnetic Resonance (CMR) imaging is a non-invasive method for assessing cardiac structure, function, and blood flow. Cine MRI extends this by capturing heart motion, providing detailed insights into cardiac mechanics. To reduce scan time and breath-hold discomfort, fast acquisition techniques have been utilized at the cost of lowering image quality. Recently, Implicit Neural Representation (INR) methods have shown promise in unsupervised reconstruction by learning coordinate-to-value mappings from undersampled data, enabling high-quality image recovery. However, current existing INR methods primarily focus on using coordinate-based positional embeddings to learn the mapping, while overlooking the feature representations of the target point and its neighboring context. In this work, we propose KP-INR, a dual-branch INR method operating in k-space for cardiac cine MRI reconstruction: one branch processes the positional embedding of k-space coordinates, while the other learns from local multi-scale k-space feature representations at those coordinates. By enabling cross-branch interaction and approximating the target k-space values from both branches, KP-INR can achieve strong performance on challenging Cartesian k-space data. Experiments on the CMRxRecon2024 dataset confirms its improved performance over baseline models and highlights its potential in this field.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lyu_crunetmruniv_2026,

title = {CRUNet-MR-Univ: A Foundation Model for Diverse Cardiac MRI Reconstruction},

author = {D. Lyu and M. Staring and H. J. Lamb and M. Doneva},

editor = {Oscar Camara and Esther Puyol Antón and Maxime Sermesant and Charlène Mauge and Marta Varela and Yingliang Ma and Rasmus Paulsen and Chengyan Wang and Qian Tao and Alistair Young},

doi = {10.1007/978-3-032-17734-6_29},

isbn = {978-3-032-17734-6},

year  = {2026},

date = {2026-07-01},

urldate = {2026-07-01},

booktitle = {Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers},

pages = {311–322},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {In recent years, deep learning has attracted increasing attention in the field of Cardiac MRI (CMR) reconstruction due to its superior performance over traditional methods, particularly in handling higher acceleration factors, highlighting its potential for real-world clinical applications. However, current deep learning methods remain limited in generalizability. CMR scans exhibit wide variability in image contrast, sampling patterns, scanner vendors, anatomical structures, and disease types. Most existing models are designed to handle only a single or narrow subset of these variations, leading to performance degradation when faced with distribution shifts. Therefore, it is beneficial to develop a unified model capable of generalizing across diverse CMR scenarios. To this end, we propose CRUNet-MR-Univ, a foundation model that leverages spatio-temporal correlations and prompt-based priors to effectively handle the full diversity of CMR scans. Our approach consistently outperforms baseline methods across a wide range of settings, highlighting its effectiveness and promise.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jabarimani_characterizing_2025a,

title = {Characterizing differences between white and gray matter T1W-based segmentations at 0.6T and 1.5T},

author = {N. Jabarimani and E. Ercan and Y. Dong and N. Pezzotti and A. Webb and P. Börnert and M. Staring and M. Osch and M. Nagtegaal},

url = {http://echo.ismrm.org/abstracts/view/c7281180-0047-444c-8ebe-65626444115c},

year  = {2025},

date = {2025-07-01},

publisher = {ISMRM},

address = {Honolulu, HI, United States of America},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jabarimani_accelerated_2025,

title = {Accelerated FLAIR imaging at 0.6T using T2W-guided multi-contrast deep learning-based reconstruction using a Zero-Shot approach},

author = {N. Jabarimani and C. Rao and E. Ercan and Y. Dong and N. Pezzotti and M. Doneva and M. Osch and M. Staring and M. Nagtegaal},

url = {http://echo.ismrm.org/abstracts/view/90100974-322d-40bc-9ebf-81e99b1dca6c},

year  = {2025},

date = {2025-07-01},

publisher = {ISMRM},

address = {Honolulu, HI, United States of America},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lyu_upcmr_2025,

title = {UPCMR: A Universal Prompt-Guided Model for Random Sampling Cardiac MRI Reconstruction},

author = {D. Lyu and C. Rao and M. Staring and M. J. P. Osch and M. Doneva and H. J. Lamb and N. Pezzotti},

editor = {Oscar Camara and Esther Puyol-Antón and Maxime Sermesant and Avan Suinesiaputra and Jichao Zhao and Chengyan Wang and Qian Tao and Alistair Young},

doi = {10.1007/978-3-031-87756-8_44},

isbn = {978-3-031-87756-8},

year  = {2025},

date = {2025-07-01},

booktitle = {Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers.},

pages = {453–463},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Cardiac magnetic resonance imaging (CMR) is vital for diagnosing heart diseases, but long scan time remains a major drawback. To address this, accelerated imaging techniques have been introduced by undersampling k-space, which reduces the quality of the resulting images. Recent deep learning advancements aim to speed up scanning while preserving quality, but adapting to various sampling modes and undersampling factors remains challenging. Therefore, building a universal model is a promising direction. In this work, we introduce UPCMR, a universal unrolled model designed for CMR reconstruction. This model incorporates two kinds of learnable prompts, undersampling-specific prompt and spatial-specific prompt, and integrates them with a UNet structure in each block. Overall, by using the CMRxRecon2024 challenge dataset for training and validation, the UPCMR model highly enhances reconstructed image quality across all random sampling scenarios through an effective training strategy compared to some traditional methods, demonstrating strong adaptability potential for this task.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{schaap2024towards,

title = {Towards Fairness in Machine Learning: Balancing Racially Imbalanced Datasets through Data Augmentation and Generative AI},

author = {A. Schaap and S. Kitharidis and N. Stein},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 16th International Joint Conference on Computational Intelligence (IJCCI)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{huang2024txgen,

title = {TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification},

author = {Q. Huang and S. Kitharidis and T. Bäck and N. Stein},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 16th International Joint Conference on Computational Intelligence (IJCCI)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}