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Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation
Abdelrahman A., Bhawna P., Jamshid M., Mohammed A, Adam J.
ArXiv 2025
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Bibtex
@article{abdallah2025rankify,
title={Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation},
author={Abdallah, Abdelrahman and Mozafari, Jamshid and Piryani, Bhawna and Ali, Mohammed and Jatowt, Adam},
journal={arXiv preprint arXiv:2502.02464},
year={2025}
}
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HintEval: A Comprehensive Framework for Hint Generation and Evaluation for Questions
Jamshid M., Bhawna P., Abdelrahman A., Adam J.
ArXiv 2025
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Bibtex
@article{mozafari2025hinteval,
title={HintEval: A Comprehensive Framework for Hint Generation and Evaluation for Questions},
author={Mozafari, Jamshid and Piryani, Bhawna and Abdallah, Abdelrahman and Jatowt, Adam},
journal={arXiv preprint arXiv:2502.00857},
year={2025}
}
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ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval
Abdelrahman A., Jamshid M., Bhawna P., Adam J.
NAACL 2025
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Bibtex
@misc{abdallah2025asrankzeroshotrerankinganswer,
title={ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval},
author={Abdelrahman Abdallah and Jamshid Mozafari and Bhawna Piryani and Adam Jatowt},
year={2025},
eprint={2501.15245},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.15245},
}
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CascadePLS-ViT: Cascade With Patch-Level Self-Supervised Vision Transformers for Breast Cancer Classification in Mammography
Abdelrahman A., Mahmoud K., Ibrahim A., Norah A., Sohail C., Ayman E.
Equal contribution: Abdelrahman A., Mahmoud K. and Ibrahim A.
ISBI 2025
Bibtex
Will be publish soon !! ..
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DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification
Abdelrahman A., Bhawna P., Jamshid M., Mohammed M., Adam J.
Coling 2025
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Bibtex
@article{abdallah2024dynrank,
title={DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification},
author={Abdallah, Abdelrahman and Mozafari, Jamshid and Piryani, Bhawna and Abdelgwad, Mohammed M and Jatowt, Adam},
journal={arXiv preprint arXiv:2412.00600},
year={2024}
}
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IHRRB-DINO: Identifying High-Risk Regions of Breast Masses in Mammogram Images Using Data-Driven Instance Noise (DINO)
Mahmoud K., Abdelrahman A., Ibrahim A., Norah A., Sohail C., Ayman E.
Equal contribution: Mahmoud K., Abdelrahman A. and Ibrahim A.
MICCAI 2024
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Bibtex
@InProceedings{101007978-3-031-72378-0_11,
author="Kasem, Mahmoud SalahEldin
and Abdallah, Abdelrahman
and Abdelhalim, Ibrahim
and Alghamdi, Norah Saleh
and Contractor, Sohail
and El-Baz, Ayman",
editor="Linguraru, Marius George
and Dou, Qi
and Feragen, Aasa
and Giannarou, Stamatia
and Glocker, Ben
and Lekadir, Karim
and Schnabel, Julia A.",
title="IHRRB-DINO: Identifying High-Risk Regions of Breast Masses in Mammogram Images Using Data-Driven Instance Noise (DINO)",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="113--122",
abstract="In this paper, we introduce IHRRB-DINO, an advanced model designed to assist radiologists in effectively detecting breast masses in mammogram images. This tool is specifically engineered to highlight high-risk regions, enhancing the capability of radiologists in identifying breast masses for more accurate and efficient assessments. Our approach incorporates a novel technique that employs Data-Driven Instance Noise (DINO) for Object Localization, which significantly improves breast mass localization. This method is augmented by data augmentation using instance-level noise during the training phase, focusing on refining the model's proficiency in precisely localizing breast masses in mammographic images. Rigorous testing and validation conducted on the BI-RADS dataset using our model, especially with the Swin-L backbone, have demonstrated promising results. We achieved an Average Precision (AP) of 46.96, indicating a substantial improvement in the accuracy and consistency of breast cancer (BC) detection and localization. These results underscore the potential of IHRRB-DINO in contributing to the advancements in computer-aided diagnosis systems for breast cancer, marking a significant stride in the field of medical imaging technology.",
isbn="978-3-031-72378-0"
}
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Detecting Temporal Ambiguity in Questions
Bhawna P., Abdelrahman A., Jamshid M., Adam J.
EMNLP Findings 2024
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Bibtex
@inproceedings{piryani-etal-2024-detecting,
title = "Detecting Temporal Ambiguity in Questions",
author = "Piryani, Bhawna and
Abdallah, Abdelrahman and
Mozafari, Jamshid and
Jatowt, Adam",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.562/",
doi = "10.18653/v1/2024.findings-emnlp.562",
pages = "9620--9634",
abstract = "Detecting and answering ambiguous questions has been a challenging task in open-domain question answering. Ambiguous questions have different answers depending on their interpretation and can take diverse forms. Temporally ambiguous questions are one of the most common types of such questions. In this paper, we introduce TEMPAMBIQA, a manually annotated temporally ambiguous QA dataset consisting of 8,162 open-domain questions derived from existing datasets. Our annotations focus on capturing temporal ambiguity to study the task of detecting temporally ambiguous questions. We propose a novel approach by using diverse search strategies based on disambiguate versions of the questions. We also introduce and test non-search, competitive baselines for detecting temporal ambiguity using zero-shot and few-shot approaches."
}
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Exploring Hint Generation Approaches in Open-Domain Question Answering
Jamshid M., Abdelrahman A., Bhawna P., Adam J.
EMNLP Findings 2024
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Bibtex
@inproceedings{mozafari-etal-2024-exploring,
title = "Exploring Hint Generation Approaches for Open-Domain Question Answering",
author = "Mozafari, Jamshid and
Abdallah, Abdelrahman and
Piryani, Bhawna and
Jatowt, Adam",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.546/",
doi = "10.18653/v1/2024.findings-emnlp.546",
pages = "9327--9352",
abstract = "Automatic Question Answering (QA) systems rely on contextual information to provide accurate answers. Commonly, contexts are prepared through either retrieval-based or generation-based methods. The former involves retrieving relevant documents from a corpus like Wikipedia, whereas the latter uses generative models such as Large Language Models (LLMs) to generate the context. In this paper, we introduce a novel context preparation approach called HINTQA, which employs Automatic Hint Generation (HG) techniques. Unlike traditional methods, HINTQA prompts LLMs to produce hints about potential answers for the question rather than generating relevant context. We evaluate our approach across three QA datasets including TriviaQA, Natural Questions, and Web Questions, examining how the number and order of hints impact performance. Our findings show that the HINTQA surpasses both retrieval-based and generation-based approaches. We demonstrate that hints enhance the accuracy of answers more than retrieved and generated contexts."
}
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ArabicaQA: A Comprehensive Dataset for Arabic Question Answering
Abdelrahman A., Mahmoud K., Mahmoud A.,Mohamed M., Mohamed E., Yasser E., Adam J.
SIGIR , 2024
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Bibtex
@misc{abdallah2024arabicaqa,
title={ArabicaQA: A Comprehensive Dataset for Arabic Question Answering},
author={Abdelrahman Abdallah and Mahmoud Kasem and Mahmoud Abdalla and Mohamed Mahmoud and Mohamed Elkasaby and Yasser Elbendary and Adam Jatowt},
year={2024},
eprint={2403.17848},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Transformers and Language Models in Form Understanding: A Comprehensive Review of Scanned Document Analysis
Abdelrahman A., Eberharter D., Pfister Z., Yasser E, Adam J.
arXiv , 2024
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Bibtex
@article{abdallah2024transformers,
title={Transformers and Language Models in Form Understanding: A Comprehensive Review of Scanned Document Analysis},
author={Abdallah, Abdelrahman and Eberharter, Daniel and Pfister, Zoe and Jatowt, Adam},
journal={arXiv preprint arXiv:2403.04080},
year={2024}
}
AMuRD: Annotated Multilingual Receipts Dataset for Cross-lingual Key Information Extraction and Classification
Abdelrahman A., Mahmoud A., Mohamed E., Yasser E., Adam J.
arXiv , 2023
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Bibtex
@misc{abdallah2023amurd,
title={AMuRD: Annotated Multilingual Receipts Dataset for Cross-lingual Key Information Extraction and Classification},
author={Abdelrahman Abdallah and Mahmoud Abdalla and Mohamed Elkasaby and Yasser Elbendary and Adam Jatowt},
year={2023},
eprint={2309.09800},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Generator-Retriever-Generator: A Novel Approach to Open-domain Question Answering
Abdelrahman A., Adam J.
arXiv , 2023
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Bibtex
@article{abdallah2023generator,
title={Generator-Retriever-Generator: A Novel Approach to Open-domain Question Answering},
author={Abdallah, Abdelrahman and Jatowt, Adam},
journal={arXiv preprint arXiv:2307.11278},
year={2023}
}
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Exploring the State of the Art in Legal QA Systems
Abdelrahman A., Bhawna P., Adam J.
Journal of Big Data , 2023
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Code & Data |
Bibtex
@misc{abdallah2023exploring,
title={Exploring the State of the Art in Legal QA Systems},
author={Abdelrahman Abdallah and Bhawna Piryani and Adam Jatowt},
year={2023},
eprint={2304.06623},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Deep learning for table detection and structure recognition: A survey
Mahmoud K., Abdelrahman A., Alexander B., Ebrahem E., Mahmoud A., Mohamed M., Mohamed H., Daniyar N., Islam T.
arXiv , 2023
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Bibtex
@misc{abdallah2023exploring,
doi = {10.48550/ARXIV.2211.08469},
url = {https://arxiv.org/abs/2211.08469},
author = {Kasem, Mahmoud and Abdallah, Abdelrahman and Berendeyev, Alexander and Elkady, Ebrahem and Abdalla, Mahmoud and Mahmoud, Mohamed and Hamada, Mohamed and Nurseitov, Daniyar and Taj-Eddin, Islam},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Deep learning for table detection and structure recognition: A survey},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
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TNCR: Table net detection and classification dataset
Abdelrahman A., Alexander B., Islam N., Daniyar N.
Neurocomputing, 2022
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Code & Data |
Bibtex
@article{abdallah2022tncr,
title={TNCR: Table net detection and classification dataset},
author={Abdallah, Abdelrahman and Berendeyev, Alexander and Nuradin, Islam and Nurseitov, Daniyar},
journal={Neurocomputing},
volume={473},
pages={79--97},
year={2022},
publisher={Elsevier}
}
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KOHTD: Kazakh offline handwritten text dataset
Nazgul T., Mahmoud K., Galymzhan A., Kairat B., Abdelrahman A., Anel A., Daniyar N.
Signal Processing: Image Communication, 2022
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Code & Data |
Bibtex
@article{toiganbayeva2022kohtd,
title={Kohtd: Kazakh offline handwritten text dataset},
author={Toiganbayeva, Nazgul and Kasem, Mahmoud and Abdimanap, Galymzhan and Bostanbekov, Kairat and Abdallah, Abdelrahman and Alimova, Anel and Nurseitov, Daniyar},
journal={Signal Processing: Image Communication},
volume={108},
pages={116827},
year={2022},
publisher={Elsevier}
}
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Attention-based fully gated CNN-BGRU for russian handwritten text
Abdelrahman A., Mohamed H., Daniyar N.
Journal of Imaging, 2020
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Bibtex
@article{abdallah2020attention,
title={Attention-based fully gated cnn-bgru for russian handwritten text},
author={Abdallah, Abdelrahman and Hamada, Mohamed and Nurseitov, Daniyar},
journal={Journal of Imaging},
volume={6},
number={12},
pages={141},
year={2020},
publisher={MDPI}
}
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Handwritten Kazakh and Russian (HKR) database for text recognition
Daniyar N., Kairat B., Daniyar K., Anel A., Abdelrahman A., Rassul Tolegenov
Multimedia Tools and Applications, 2021
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Code & Data |
Bibtex
@article{nurseitov2021handwritten,
title={Handwritten Kazakh and Russian (HKR) database for text recognition},
author={Nurseitov, Daniyar and Bostanbekov, Kairat and Kurmankhojayev, Daniyar and Alimova, Anel and Abdallah, Abdelrahman and Tolegenov, Rassul},
journal={Multimedia Tools and Applications},
volume={80},
number={21},
pages={33075--33097},
year={2021},
publisher={Springer}
}
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Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models
Daniyar N., Kairat B., Maksat K., Anel A., Abdelrahman A., Galymzhan A.
Advances in Science, Technology and Engineering Systems Journal (ASTESJ), 2020
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Bibtex
@article{DaniyarNurseitovKairatBostanbekovMaksatKanatovAnelAlimovaAbdelrahmanAbdallah2020,
author = {{Daniyar Nurseitov, Kairat Bostanbekov, Maksat Kanatov, Anel Alimova, Abdelrahman Abdallah}, Galymzhan Abdimanap},
doi = {10.25046/aj0505114},
file = {:D$\backslash$:/ASTESJ/ASTESJ{\_}0505114.pdf:pdf},
journal = {Advances in Science, Technology and Engineering Systems Journal},
keywords = {CNN,CTC,Convolutional neural networks,RNN,Recurrent neural networks},
number = {5},
pages = {934--943},
title = {{Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models}},
volume = {5},
year = {2020}
}
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Automated Question Answer medical model based on Deep Learning Technology
Abdelrahman A., Mahmoud K., Mohamed H., Shaymaa S.
ICEMIS'20: Proceedings of the 6th International Conference on Engineering & MIS, 2020
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Bibtex
@inproceedings{abdallah2020automated,
title={Automated question-answer medical model based on deep learning technology},
author={Abdallah, Abdelrahman and Kasem, Mahmoud and Hamada, Mohamed A and Sdeek, Shaymaa},
booktitle={Proceedings of the 6th International Conference on Engineering \& MIS 2020},
pages={1--8},
year={2020}
}
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Neural network estimation model to optimize timing and schedule of software projects
Mohamed A. H., Abdelrahman A., Mahmoud K., Mohamed A.
2021 IEEE International Conference on Smart Information Systems and Technologies (SIST), 2021
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Bibtex
@inproceedings{hamada2021neural,
title={Neural network estimation model to optimize timing and schedule of software projects},
author={Hamada, Mohamed A and Abdallah, Abdelrahman and Kasem, Mahmoud and Abokhalil, Mohamed},
booktitle={2021 IEEE International Conference on Smart Information Systems and Technologies (SIST)},
pages={1--7},
year={2021},
organization={IEEE}
}
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AE-LSTM: Autoencoder with LSTM-Based Intrusion Detection in IoT
Mohamed M., Mahmoud K., Abdelrahman A., Hyun S. K.
2022 International Telecommunications Conference (ITC-Egypt), 2022
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Bibtex
@inproceedings{mahmoud2022ae,
title={AE-LSTM: Autoencoder with LSTM-Based Intrusion Detection in IoT},
author={Mahmoud, Mohamed and Kasem, Mahmoud and Abdallah, Abdelrahman and Kang, Hyun Soo},
booktitle={2022 International Telecommunications Conference (ITC-Egypt)},
pages={1--6},
year={2022},
organization={IEEE}
}
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Enhancing Core Image Classification Using Generative Adversarial Networks (GANs)
Galymzhan A., Kairat B., Abdelrahman A. , Anel A., Darkhan K., Daniyar N.
arXiv, 2022
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Link |
Bibtex
@article{nurseitov2022application,
title={Enhancing Core Image Classification Using Generative Adversarial Networks (GANs) },
author={Nurseitov, Daniyar and Bostanbekov, Kairat and Abdimanap, Galymzhan and Abdallah, Abdelrahman and Alimova, Anel and Kurmangaliyev, Darkhan},
journal={arXiv preprint arXiv:2204.14224},
year={2022}
}
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Developing Predictive Oil Well Diagnostics Based on Intelligent Algorithms
Zhanar O., Daur A., Aslan A., Abdelrahman A.
2021 IEEE International Conference on Smart Information Systems and Technologies (SIST), 2021
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Bibtex
@inproceedings{omirbekova2021developing,
title={Developing Predictive Oil Well Diagnostics Based on Intelligent Algorithms},
author={Omirbekova, Zhanar and Aktaukenov, Daur and Amangeldiyev, Aslan and Abdallah, Abdelrahman},
booktitle={2021 IEEE International Conference on Smart Information Systems and Technologies (SIST)},
pages={1--7},
year={2021},
organization={IEEE}
}
