Publications

@article{duggan2025advancing,
  title = {Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellites},
  author = {Aidan Duggan and Bruno Andrade and Haithem Afli},
  journal = {European Journal of Remote Sensing, 58(1), 2567921},
  year = {2025},
  publisher = {Taylor & Francis (Informa UK)},
  doi = {10.1080/22797254.2025.2567921},
  url = {https://doi.org/10.1080/22797254.2025.2567921},
}

@inproceedings{krause2025reads,
  title = {From Reads to Reports: A Vision for a GFM-Powered Genomic Diagnostic Platform},
  author = {Thomas Krause and Philippe Tamla and Andrea Leoni and Flavia Monti and Francesca De Luzi and Jamie FitzGerald and Bruno Andrade and Haithem Afli and Massimo Mecella and Paolo Buono and Andrea Molinari and Matthias Hemmje},
  booktitle = {IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2025)},
  year = {2025},
  pages = {7207–7213},
  publisher = {IEEE},
  doi = {10.1109/bibm66473.2025.11356528},
  url = {https://doi.org/10.1109/bibm66473.2025.11356528},
}

@inproceedings{zadykian2025explainable,
  title = {Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs},
  author = {Vadim Zadykian and Bruno Andrade and Haithem Afli},
  booktitle = {RecSys in HR'25: 5th Workshop on Recommender Systems for Human Resources (ACM RecSys 2025)},
  year = {2025},
  publisher = {CEUR-WS},
  eprint = {2509.09522}, archivePrefix = {arXiv},
  url = {https://ceur-ws.org/Vol-4046/},
}

@inproceedings{artiaga2024influence,
  title = {The Influence of Iconicity in Transfer Learning for Sign Language Recognition},
  author = {Keren Artiaga and Conor Lynch and Haithem Afli and Mohammed Hasanuzzaman},
  booktitle = {Int. Conf. on Applications of Natural Language to Information Systems (NLDB), Springer},
  year = {2024},
  pages = {226–240},
  publisher = {Springer Nature},
  doi = {10.1007/978-3-031-70239-6_16},
  url = {https://doi.org/10.1007/978-3-031-70239-6_16},
}

@incollection{afli2023decision,
  title = {A Decision-Level Approach to Multimodal Sentiment Analysis},
  author = {Haithem Afli and Jason Burns and Andy Way},
  booktitle = {Computational Linguistics and Intelligent Text Processing (CICLing 2018), Lecture Notes in Computer Science},
  year = {2023},
}

@article{joshi2023enabling,
  title = {Enabling All In-Edge Deep Learning: A Literature Review},
  author = {Praveen Joshi and Mohammed Hasanuzzaman and Chandra Thapa and Haithem Afli and Ted Scully},
  journal = {IEEE Access, 11, 3431–3460},
  year = {2023},
  publisher = {IEEE},
  doi = {10.1109/ACCESS.2023.3234110},
  url = {https://doi.org/10.1109/ACCESS.2023.3234110},
}

The advent of Multilingual Language Models (MLLMs) and Large Language Models (LLMs) has spawned innovation in many areas of natural language processing. Despite the exciting potential of this technology, its impact on developing high-quality Machine Translation (MT) outputs for low-resource languages remains relatively under-explored. Furthermore, an open-source application, dedicated to both fine-tuning MLLMs and managing the complete MT workflow for low-resources languages, remains unavailable. We aim to address these imbalances through the development of adaptMLLM, which streamlines all processes involved in the fine-tuning of MLLMs for MT. This open-source application is tailored for developers, translators, and users who are engaged in MT. It is particularly useful for newcomers to the field, as it significantly streamlines the configuration of the development environment. An intuitive interface allows for easy customisation of hyperparameters, and the application offers a range of metrics for model evaluation and the capability to deploy models as a translation service directly within the application. As a multilingual tool, we used adaptMLLM to fine-tune models for two low-resource language pairs: English to Irish (EN↔GA) and English to Marathi (EN↔MR). Compared with baselines from the LoResMT2021 Shared Task, the adaptMLLM system demonstrated significant improvements. In the EN→GA direction, an improvement of 5.2 BLEU points was observed and an increase of 40.5 BLEU points was recorded in the GA→EN direction representing relative improvements of 14% and 117%, respectively. Significant improvements in the translation performance of the EN↔MR pair were also observed notably in the MR→EN direction with an increase of 21.3 BLEU points which corresponds to a relative improvement of 68%. Finally, a fine-grained human evaluation of the MLLM output on the EN→GA pair was conducted using the Multidimensional Quality Metrics and Scalar Quality Metrics error taxonomies. The application and models are freely available.

@article{lankford2023adaptmllm,
  title = {adaptMLLM: Fine-Tuning Multilingual Language Models on Low-Resource Languages with Integrated LLM Playgrounds},
  author = {Séamus Lankford and Haithem Afli and Andy Way},
  journal = {Information, 14(12), 638},
  year = {2023},
  publisher = {MDPI},
  doi = {10.3390/info14120638},
  url = {https://doi.org/10.3390/info14120638},
}

@article{lankford2023adaptnmt,
  title = {adaptNMT: An Open-Source, Language-Agnostic Development Environment for Neural Machine Translation},
  author = {Séamus Lankford and Haithem Afli and Andy Way},
  journal = {Language Resources and Evaluation, 57(4), 1671–1696},
  year = {2023},
  publisher = {Springer},
  doi = {10.1007/s10579-023-09671-2},
  url = {https://doi.org/10.1007/s10579-023-09671-2},
}

@article{leske2022bigami,
  title = {BiGAMi: Bi-Objective Genetic Algorithm Fitness Function for Feature Selection on Microbiome Datasets},
  author = {Mike Leske and Francesca Bottacini and Haithem Afli and Bruno G. N. Andrade},
  journal = {Methods and Protocols, 5(3), 42},
  year = {2022},
  url = {https://www.mdpi.com/2409-9279/5/3/42},
}

@article{lankford2022human,
  title = {Human Evaluation of English–Irish Transformer-Based NMT},
  author = {Séamus Lankford and Haithem Afli and Andy Way},
  journal = {Information, 13(7), 309},
  year = {2022},
  url = {https://www.mdpi.com/2078-2489/13/7/309},
}

Machine learning (ML) in healthcare data analytics is attracting much attention because of the unprecedented power of ML to extract knowledge that improves the decision-making process. At the same time, laws and ethics codes drafted by countries to govern healthcare data are becoming stringent. Although healthcare practitioners are struggling with an enforced governance framework, we see the emergence of distributed learning-based frameworks disrupting traditional-ML-model development. Splitfed learning (SFL) is one of the recent developments in distributed machine learning that empowers healthcare practitioners to preserve the privacy of input data and enables them to train ML models. However, SFL has some extra communication and computation overheads at the client side due to the requirement of client-side model synchronization. For a resource-constrained client side (hospitals with limited computational powers), removing such conditions is required to gain efficiency in the learning. In this regard, this paper studies SFL without client-side model synchronization. The resulting architecture is known as multi-head split learning (MHSL). At the same time, it is important to investigate information leakage, which indicates how much information is gained by the server related to the raw data directly out of the smashed data—the output of the client-side model portion—passed to it by the client. Our empirical studies examine the Resnet-18 and Conv1-D architecture model on the ECG and HAM-10000 datasets under IID data distribution. The results find that SFL provides 1.81% and 2.36% better accuracy than MHSL on the ECG and HAM-10000 datasets, respectively (for cut-layer value set to 1). Analysis of experimentation with various client-side model portions demonstrates that it has an impact on the overall performance. With an increase in layers in the client-side model portion, SFL performance improves while MHSL performance degrades. Experiment results also demonstrate that information leakage provided by mutual information score values in SFL is more than MHSL for ECG and HAM-10000 datasets by 2×10−5 and 4×10−3, respectively.

@article{joshi2022performance,
  title = {Performance and Information Leakage in Splitfed Learning and Multi-Head Split Learning in Healthcare Data and Beyond},
  author = {Praveen Joshi and Chandra Thapa and Seyit Camtepe and Mohammed Hasanuzzaman and Ted Scully and Haithem Afli},
  journal = {Methods and Protocols, 5(4), 60},
  year = {2022},
  publisher = {MDPI},
  doi = {10.3390/mps5040060},
  url = {https://doi.org/10.3390/mps5040060},
}

@article{andrade2022stool,
  title = {Stool and Ruminal Microbiome Components Associated with Methane Emission and Feed Efficiency in Nelore Beef Cattle},
  author = {Bruno G. N. Andrade and Flavia A. Bressani and Rafael R. C. Cuadrat and Tainã F. Cardoso and Jessica M. Malheiros and Priscila S. N. de Oliveira and Juliana Petrini and Gerson B. Mourão and Luiz L. Coutinho and James M. Reecy and Luciana C. A. Regitano and Haithem Afli},
  journal = {Frontiers in Genetics, 13, 812828},
  year = {2022},
  publisher = {Frontiers Media},
  doi = {10.3389/fgene.2022.812828},
  url = {https://doi.org/10.3389/fgene.2022.812828},
}

@inproceedings{vu2022continuous,
  title = {Towards Continuous Professional Monitoring of Health Status Based on Energetic Balancing},
  author = {Binh Vu and Sebastian Bruchhaus and Anne Moorhead and Huiru Zheng and Luigi D'Arco and Louise Lynch and Luigia Simona Sica and Michela Ponticorvo and Federico Diano and Haithem Afli},
  booktitle = {2022 IEEE International Workshop on Sport, Technology and Research (STAR), pp. 72–77},
  year = {2022},
  pages = {72–77},
  url = {https://ieeexplore.ieee.org/document/9859599},
}

@incollection{joshi2021affective,
  title = {Affective Analytics and Visualization for Ensemble Event-Driven Stock Market Forecasting},
  author = {Praveen Joshi and Haithem Afli},
  booktitle = {Advanced Visual Interfaces: Supporting AI and Big Data Applications (AVI 2020 Workshops), Springer, pp. 28–55},
  year = {2021},
  pages = {28–55},
  doi = {10.1007/978-3-030-68007-7_3},
  url = {https://doi.org/10.1007/978-3-030-68007-7_3},
}

@inproceedings{lankford2021machine,
  title = {Machine Translation in the Covid Domain: an English–Irish Case Study for LoResMT 2021},
  author = {Séamus Lankford and Haithem Afli and Andy Way},
  booktitle = {Workshop on Technologies for MT of Low Resource Languages (LoResMT 2021)},
  year = {2021},
  eprint = {2403.01196}, archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2403.01196},
}

@article{ventero2021nasopharyngeal,
  title = {Nasopharyngeal Microbial Communities of Patients Infected with SARS-CoV-2 That Developed COVID-19},
  author = {Maria Paz Ventero and Rafael R. C. Cuadrat and Inmaculada Vidal and Bruno G. N. Andrade and Carmen Molina-Pardines and Jose M. Haro-Moreno and Felipe H. Coutinho and Esperanza Merino and Luciana C. A. Regitano and Cynthia B. Silveira and Haithem Afli and Mario López-Pérez and Juan Carlos Rodríguez},
  journal = {Frontiers in Microbiology, 12, 637430},
  year = {2021},
  doi = {10.3389/fmicb.2021.637430},
  url = {https://doi.org/10.3389/fmicb.2021.637430},
}

@article{andrade2021putative,
  title = {Putative Mobilized Colistin Resistance Genes in the Human Gut Microbiome},
  author = {Bruno G. N. Andrade and Tobias Goris and Haithem Afli and Felipe H. Coutinho and Alberto M. R. Dávila and Rafael R. C. Cuadrat},
  journal = {BMC Microbiology, 21, 1–10},
  year = {2021},
  publisher = {Springer Nature (BMC)},
  doi = {10.1186/s12866-021-02281-4},
  url = {https://doi.org/10.1186/s12866-021-02281-4},
}

@inproceedings{considine2020automatic,
  title = {Automatic Error Detection for Image to Text Tools},
  author = {Suzanne Considine and Haithem Afli and Andy Way},
  booktitle = {6th Collaborative European Research Conference (CERC 2020), pp. 183–190},
  year = {2020},
  pages = {183–190},
  url = {https://ceur-ws.org/Vol-2815/CERC2020_short02.pdf},
}

@inproceedings{wu2020chatbot,
  title = {Chatbot-Based Behaviour Analysis for Obesity Support Platform},
  author = {Yanxin Wu and Ryan Donovan and Binh Vu and Felix Engel and Matthias Hemmje and Haithem Afli},
  booktitle = {6th Collaborative European Research Conference (CERC 2020), pp. 112–124},
  year = {2020},
  pages = {112–124},
  url = {https://ceur-ws.org/Vol-2815/},
}

@inproceedings{doshi2020common,
  title = {Common Sense Validation and Reasoning Using Natural Language Processing},
  author = {Shrenik Doshi and Praveen Joshi and Haithem Afli},
  booktitle = {6th Collaborative European Research Conference (CERC 2020), pp. 141–151},
  year = {2020},
  pages = {141–151},
  url = {https://ceur-ws.org/Vol-2815/},
}

@inproceedings{chudiiwueze2020detecting,
  title = {Detecting Sarcasm in News Headlines},
  author = {Onyinye Chudi-Iwueze and Haithem Afli},
  booktitle = {6th Collaborative European Research Conference (CERC 2020)},
  year = {2020},
  url = {https://ceur-ws.org/Vol-2815/CERC2020_paper06.pdf},
}

@inproceedings{lohar2020dialect,
  title = {Dialect-Aware Tokenisation for Translating Arabic User-Generated Content},
  author = {Pintu Lohar and Haithem Afli and Andy Way},
  booktitle = {30th Meeting of Computational Linguistics in the Netherlands (CLIN 30)},
  year = {2020},
  url = {https://clin30.sites.uu.nl/accepted-submissions/},
}

@inproceedings{juarez2020online,
  title = {Online News Analysis on Cloud Computing Platform for Market Prediction},
  author = {Claudia Juarez and Haithem Afli},
  booktitle = {6th Collaborative European Research Conference (CERC 2020), pp. 125–140},
  year = {2020},
  pages = {125–140},
  url = {https://ceur-ws.org/Vol-2815/CERC2020_paper08.pdf},
}

@inproceedings{chandaliya2020telestroke,
  title = {TeleStroke System (TSS)},
  author = {Rishabh Chandaliya and Praveen Joshi and Haithem Afli},
  booktitle = {6th Collaborative European Research Conference (CERC 2020), pp. 325–334},
  year = {2020},
  pages = {325–334},
  url = {https://ceur-ws.org/Vol-2815/CERC2020_paper20.pdf},
}

@incollection{krause2020understanding,
  title = {Understanding the Role of (Advanced) Machine Learning in Metagenomic Workflows},
  author = {Thomas Krause and Bruno G. N. Andrade and Haithem Afli and Haiying Wang and Huiru Zheng and Matthias L. Hemmje},
  booktitle = {AVI-BDA 2020 / ITAVIS 2020, Lecture Notes in Computer Science, vol. 12585, Springer},
  year = {2020},
  doi = {10.1007/978-3-030-68007-7_4},
  url = {https://doi.org/10.1007/978-3-030-68007-7_4},
}

@inproceedings{quinlan2019comparative,
  title = {A Comparative Analysis of Classification Techniques for Cervical Cancer Utilising At Risk Factors and Screening Test Results},
  author = {Sean Quinlan and Haithem Afli and Ruairi O'Reilly},
  booktitle = {27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2019), Galway},
  year = {2019},
  pages = {400–411},
  url = {https://ceur-ws.org/Vol-2563/aics_37.pdf},
}

@inproceedings{vu2019metagenomic,
  title = {A Metagenomic Content and Knowledge Management Ecosystem Platform},
  author = {Binh Vu and Yanxin Wu and Haithem Afli and Paul Mc Kevitt and Paul Walsh and Felix Engel and Michael Fuchs and Matthias Hemmje},
  booktitle = {IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019)},
  year = {2019},
}

@inproceedings{lohar2019systematic,
  title = {A Systematic Comparison Between SMT and NMT on Translating User-Generated Content},
  author = {Pintu Lohar and Maja Popović and Haithem Afli and Andy Way},
  booktitle = {20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2019)},
  year = {2019},
  url = {https://core.ac.uk/download/pdf/237018176.pdf},
}

@inproceedings{lohar2019sentiment,
  title = {Sentiment Preservation in Machine Translation},
  author = {Pintu Lohar and Haithem Afli and Andy Way},
  booktitle = {29th Computational Linguistics in the Netherlands (CLIN 29)},
  year = {2019},
  url = {http://www.let.rug.nl/clin29/Pintu_Lohar_Haithem_Afli_Andy_Way.php},
}

@inproceedings{ganguly2018word,
  title = {Word Embedding-Based Semantic Cross-Lingual Document Alignment in Comparable Corpora},
  author = {Debasis Ganguly and Haithem Afli and Dwaipayan Roy},
  booktitle = {10th Annual Meeting of the Forum for Information Retrieval Evaluation (FIRE 2018), ACM, pp. 28–34},
  year = {2018},
  pages = {28–34},
  doi = {10.1145/3293339.3293346},
  url = {https://doi.org/10.1145/3293339.3293346},
}

@article{lohar2017maintaining,
  title = {Maintaining Sentiment Polarity in Translation of User-Generated Content},
  author = {Pintu Lohar and Haithem Afli and Andy Way},
  journal = {The Prague Bulletin of Mathematical Linguistics, 108(1), 73–84},
  year = {2017},
  pages = {73–84},
}

@article{afli2017sentiment,
  title = {Sentiment Translation for Low-Resourced Languages: Experiments on Irish General Election Tweets},
  author = {Haithem Afli and Sorcha McGuire and Andy Way},
  journal = {The International Journal of Computational Linguistics and Applications (IJCLA)},
  year = {2017},
  url = {https://core.ac.uk/display/200762302},
}

In recent decades, statistical approaches have significantly advanced the development of machine translation systems. However, the applicability of these methods directly depends on the availability of very large quantities of parallel data. Recent works have demonstrated that a comparable corpus can compensate for the shortage of parallel corpora. In this paper, we propose an alternative to comparable corpora containing text documents as resources for extracting parallel data: a multimodal comparable corpus with audio documents in source language and text document in target language, built from Euronews and TED web sites. The audio is transcribed by an automatic speech recognition system, and translated with a baseline statistical machine translation system. We then use information retrieval in a large text corpus in the target language in order to extract parallel sentences/phrases. We evaluate the quality of the extracted data on an English to French translation task and show significant improvements over a state-of-the-art baseline.

@article{afli2016building,
  title = {Building and Using Multimodal Comparable Corpora for Machine Translation},
  author = {Haithem Afli and Loïc Barrault and Holger Schwenk},
  journal = {Natural Language Engineering, 22(4), 603–625},
  year = {2016},
  pages = {603–625},
  publisher = {Cambridge University Press},
  doi = {10.1017/s1351324916000152},
  url = {https://doi.org/10.1017/s1351324916000152},
}

@article{lohar2016fada,
  title = {FaDA: Fast Document Aligner Using Word Embedding},
  author = {Pintu Lohar and Debasis Ganguly and Haithem Afli and Andy Way and Gareth J. F. Jones},
  journal = {The Prague Bulletin of Mathematical Linguistics, 106, 169–179},
  year = {2016},
  pages = {169–179},
  doi = {10.1515/pralin-2016-0016},
  url = {https://doi.org/10.1515/pralin-2016-0016},
}

@inproceedings{afli2016arabic,
  title = {From Arabic User-Generated Content to Machine Translation: Integrating Automatic Error Correction},
  author = {Haithem Afli and Walid Aransa and Pintu Lohar and Andy Way},
  booktitle = {CICLing 2016: 17th International Conference on Intelligent Text Processing and Computational Linguistics},
  year = {2016},
  url = {https://www.computing.dcu.ie/~away/PUBS/2016/Haithem.pdf},
}

@inproceedings{afli2015ocr,
  title = {OCR Error Correction Using Statistical Machine Translation},
  author = {Haithem Afli and Loïc Barrault and Holger Schwenk},
  booktitle = {16th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2015)},
  year = {2015},
}

@inproceedings{afli2014multimodal,
  title = {Multimodal Comparable Corpora for Machine Translation},
  author = {Haithem Afli and Loïc Barrault and Holger Schwenk},
  booktitle = {7th Workshop on Building and Using Comparable Corpora (BUCC 2014)},
  year = {2014},
}

@incollection{afli2012parallel,
  title = {Parallel Text Extraction from Multimodal Comparable Corpora},
  author = {Haithem Afli and Loïc Barrault and Holger Schwenk},
  booktitle = {8th International Conference on Natural Language Processing (JapTAL/NLP 2012), LNAI 7614, Springer, pp. 40–51},
  year = {2012},
  pages = {40–51},
  doi = {10.1007/978-3-642-33983-7_5},
  url = {https://doi.org/10.1007/978-3-642-33983-7_5},
}

@inproceedings{afli2012traduction,
  title = {Traduction automatique à partir de corpus comparables : Extraction de phrases parallèles à partir de données comparables multimodales},
  author = {Haithem Afli and Loïc Barrault and Holger Schwenk},
  booktitle = {TALN 2012, Grenoble, France},
  year = {2012},
}

@inproceedings{besacier2010lig,
  title = {LIG Statistical Machine Translation Systems for IWSLT 2010},
  author = {Laurent Besacier and Haithem Afli and Do Thi Ngoc Diep and Hervé Blanchon and Marion Potet},
  booktitle = {International Workshop on Spoken Language Translation (IWSLT 2010)},
  year = {2010},
  url = {https://hal.archives-ouvertes.fr/hal-00959170},
}

@misc{zebrowski2024predicting,
  title = {Predicting Country Instability Using Bayesian Deep Learning and Random Forest},
  author = {Adam Zebrowski and Haithem Afli},
  howpublished = {arXiv preprint},
  year = {2024},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2411.06639},
  eprint = {2411.06639}, archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arXiv.2411.06639},
}

@misc{lankford2024transformers,
  title = {Transformers for Low-Resource Languages: Is Féidir Linn!},
  author = {Séamus Lankford and Haithem Afli and Andy Way},
  howpublished = {arXiv preprint arXiv:2403.01985},
  year = {2024},
  eprint = {2403.01985}, archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2403.01985},
}

@misc{joshi2023federated,
  title = {Federated Split Learning with Only Positive Labels for Resource-Constrained IoT Environments},
  author = {Praveen Joshi and Chandra Thapa and Mohammed Hasanuzzaman and Ted Scully and Haithem Afli},
  howpublished = {arXiv preprint arXiv:2307.13266},
  year = {2023},
  eprint = {2307.13266}, archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2307.13266},
}

@misc{joshi2022enabling,
  title = {Enabling Deep Learning for All-in Edge Paradigm},
  author = {Praveen Joshi and Haithem Afli and Mohammed Hasanuzzaman and Chandra Thapa and Ted Scully},
  howpublished = {CoRR / arXiv preprint arXiv:2204.03326},
  year = {2022},
  eprint = {2204.03326}, archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2204.03326},
}

@misc{joshi2021splitfed,
  title = {Splitfed Learning Without Client-Side Synchronization: Analyzing Client-Side Split Network Portion Size to Overall Performance},
  author = {Praveen Joshi and Chandra Thapa and Seyit Camtepe and Mohammed Hasanuzzaman and Ted Scully and Haithem Afli},
  howpublished = {arXiv preprint arXiv:2109.09246},
  year = {2021},
  eprint = {2109.09246}, archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2109.09246},
}

@proceedings{afli2020proceedings,
  title = {Proceedings of the 6th Collaborative European Research Conference (CERC 2020)},
  author = {Haithem Afli and Udo Bleimann and Dirk Burkhardt and Robert Loew and Stefanie Regier and Ingo Stengel and Haiying Wang and Huiru Zheng},
  title = {CEUR Workshop Proceedings, Vol-2815},
  year = {2020},
  url = {https://ceur-ws.org/Vol-2815/},
}

@proceedings{afli2017proceedings,
  title = {Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora},
  author = {Haithem Afli and Chao-Hong Liu},
  title = {First Workshop on Curation and Applications of Parallel and Comparable Corpora, IJCNLP 2017},
  year = {2017},
}

@techreport{alam2022manual,
  title = {D5.3 Manual for Software Prototype of Social Media Analysis Tool to Monitor Public Attitudes towards Migration},
  author = {Mehwish Alam and Yiyi Chen and Haithem Afli},
  institution = {ITFLOWS Project Deliverable (H2020)},
  year = {2022},
  url = {https://www.itflows.eu/wp-content/uploads/2022/06/27.-D5.3-ITFLOWS.pdf},
}

@techreport{afli2021details,
  title = {D3.3 Details of the Big Data Used in Predicting Country Instability and Model Description},
  author = {Haithem Afli and Adam Zebrowski and Mehwish Alam and Yiyi Chen},
  institution = {ITFLOWS Project Deliverable (H2020)},
  year = {2021},
  url = {https://www.itflows.eu/wp-content/uploads/2022/06/22.-D3.3-ITFLOWS.pdf},
}

@techreport{afli2017dublin,
  title = {Dublin City University Participation in the VTT Track at TRECVid 2017},
  author = {Haithem Afli and Feiyan Hu and Jinhua Du and Daniel Cosgrove and Kevin McGuinness and Noel E. O'Connor and Eric Arazo Sanchez and Jiang Zhou and Alan F. Smeaton},
  institution = {Proceedings of TRECVid 2017, NIST},
  year = {2017},
}

@techreport{marsden2016dublin,
  title = {Dublin City University and Partners' Participation in the INS and VTT Tracks at TRECVid 2016},
  author = {Mark Marsden and Eva Mohedano and Kevin McGuinness and Andrea Calafell and Xavier Giró-i-Nieto and Noel E. O'Connor and Jiang Zhou and Lucas Azevedo and Tobias Daudert and Brian Davis and Manuela Hürlimann and Haithem Afli and Jinhua Du and Debasis Ganguly and Wei Li and Andy Way and Alan F. Smeaton},
  institution = {2016 TREC Video Retrieval Evaluation Workshop (TRECVid 2016), NIST},
  year = {2016},
}