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1. Docling Technical Report

Author

Auer, Christoph, Lysak, Maksym, Nassar, Ahmed, Dolfi, Michele, Livathinos, Nikolaos, Vagenas, Panos, Ramis, Cesar Berrospi, Omenetti, Matteo, Lindlbauer, Fabian, Dinkla, Kasper, Mishra, Lokesh, Kim, Yusik, Gupta, Shubham, de Lima, Rafael Teixeira, Weber, Valery, Morin, Lucas, Meijer, Ingmar, Kuropiatnyk, Viktor, and Staar, Peter W. J.

Subjects
Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Software Engineering
Abstract

This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models.

Published
2024

2. KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents

Author

Naparstek, Oshri, Pony, Roi, Shapira, Inbar, Dahood, Foad Abo, Azulai, Ophir, Yaroker, Yevgeny, Rubinstein, Nadav, Lysak, Maksym, Staar, Peter, Nassar, Ahmed, Livathinos, Nikolaos, Auer, Christoph, Amrani, Elad, Friedman, Idan, Prince, Orit, Burshtein, Yevgeny, Goldfarb, Adi Raz, and Barzelay, Udi

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

In recent years, the challenge of extracting information from business documents has emerged as a critical task, finding applications across numerous domains. This effort has attracted substantial interest from both industry and academy, highlighting its significance in the current technological landscape. Most datasets in this area are primarily focused on Key Information Extraction (KIE), where the extraction process revolves around extracting information using a specific, predefined set of keys. Unlike most existing datasets and benchmarks, our focus is on discovering key-value pairs (KVPs) without relying on predefined keys, navigating through an array of diverse templates and complex layouts. This task presents unique challenges, primarily due to the absence of comprehensive datasets and benchmarks tailored for non-predetermined KVP extraction. To address this gap, we introduce KVP10k , a new dataset and benchmark specifically designed for KVP extraction. The dataset contains 10707 richly annotated images. In our benchmark, we also introduce a new challenging task that combines elements of KIE as well as KVP in a single task. KVP10k sets itself apart with its extensive diversity in data and richly detailed annotations, paving the way for advancements in the field of information extraction from complex business documents., Comment: accepted ICDAR2024

Published
2024

6. ESG Accountability Made Easy: DocQA at Your Service

Author

Mishra, Lokesh, Berrospi, Cesar, Dinkla, Kasper, Antognini, Diego, Fusco, Francesco, Bothur, Benedikt, Lysak, Maksym, Livathinos, Nikolaos, Nassar, Ahmed, Vagenas, Panagiotis, Morin, Lucas, Auer, Christoph, Dolfi, Michele, and Staar, Peter

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

We present Deep Search DocQA. This application enables information extraction from documents via a question-answering conversational assistant. The system integrates several technologies from different AI disciplines consisting of document conversion to machine-readable format (via computer vision), finding relevant data (via natural language processing), and formulating an eloquent response (via large language models). Users can explore over 10,000 Environmental, Social, and Governance (ESG) disclosure reports from over 2000 corporations. The Deep Search platform can be accessed at: https://ds4sd.github.io., Comment: Accepted at the Demonstration Track of the 38th Annual AAAI Conference on Artificial Intelligence (AAAI 24)

Published
2023
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7. MolGrapher: Graph-based Visual Recognition of Chemical Structures

Author

Morin, Lucas, Danelljan, Martin, Agea, Maria Isabel, Nassar, Ahmed, Weber, Valery, Meijer, Ingmar, Staar, Peter, and Yu, Fisher

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The automatic analysis of chemical literature has immense potential to accelerate the discovery of new materials and drugs. Much of the critical information in patent documents and scientific articles is contained in figures, depicting the molecule structures. However, automatically parsing the exact chemical structure is a formidable challenge, due to the amount of detailed information, the diversity of drawing styles, and the need for training data. In this work, we introduce MolGrapher to recognize chemical structures visually. First, a deep keypoint detector detects the atoms. Second, we treat all candidate atoms and bonds as nodes and put them in a graph. This construct allows a natural graph representation of the molecule. Last, we classify atom and bond nodes in the graph with a Graph Neural Network. To address the lack of real training data, we propose a synthetic data generation pipeline producing diverse and realistic results. In addition, we introduce a large-scale benchmark of annotated real molecule images, USPTO-30K, to spur research on this critical topic. Extensive experiments on five datasets show that our approach significantly outperforms classical and learning-based methods in most settings. Code, models, and datasets are available.

Published
2023
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10. ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents

Author

Auer, Christoph, Nassar, Ahmed, Lysak, Maksym, Dolfi, Michele, Livathinos, Nikolaos, and Staar, Peter

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Transforming documents into machine-processable representations is a challenging task due to their complex structures and variability in formats. Recovering the layout structure and content from PDF files or scanned material has remained a key problem for decades. ICDAR has a long tradition in hosting competitions to benchmark the state-of-the-art and encourage the development of novel solutions to document layout understanding. In this report, we present the results of our \textit{ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents}, which posed the challenge to accurately segment the page layout in a broad range of document styles and domains, including corporate reports, technical literature and patents. To raise the bar over previous competitions, we engineered a hard competition dataset and proposed the recent DocLayNet dataset for training. We recorded 45 team registrations and received official submissions from 21 teams. In the presented solutions, we recognize interesting combinations of recent computer vision models, data augmentation strategies and ensemble methods to achieve remarkable accuracy in the task we posed. A clear trend towards adoption of vision-transformer based methods is evident. The results demonstrate substantial progress towards achieving robust and highly generalizing methods for document layout understanding., Comment: ICDAR 2023, 10 pages, 4 figures

Published
2023
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11. Optimized Table Tokenization for Table Structure Recognition

Author

Lysak, Maksym, Nassar, Ahmed, Livathinos, Nikolaos, Auer, Christoph, and Staar, Peter

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs., Comment: Accepted to ICDAR 2023, 12 pages, 6 figures

Published
2023

15. DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis

Author

Pfitzmann, Birgit, Auer, Christoph, Dolfi, Michele, Nassar, Ahmed S, and Staar, Peter W J

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present \textit{DocLayNet}, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10\% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNet-trained models are more robust and thus the preferred choice for general-purpose document-layout analysis., Comment: 9 pages, 6 figures, 5 tables. Accepted paper at SIGKDD 2022 conference

Published
2022
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16. Multilevel sentiment analysis in arabic

Author

Nassar, Ahmed and Sezer, Ebru

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

In this study, we aimed to improve the performance results of Arabic sentiment analysis. This can be achieved by investigating the most successful machine learning method and the most useful feature vector to classify sentiments in both term and document levels into two (positive or negative) categories. Moreover, specification of one polarity degree for the term that has more than one is investigated. Also to handle the negations and intensifications, some rules are developed. According to the obtained results, Artificial Neural Network classifier is nominated as the best classifier in both term and document level sentiment analysis (SA) for Arabic Language. Furthermore, the average F-score achieved in the term level SA for both positive and negative testing classes is 0.92. In the document level SA, the average F-score for positive testing classes is 0.94, while for negative classes is 0.93., Comment: 10 pages, 3 figures, Published in: 2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE), Date of Conference: 26-27 March 2019

Published
2022

17. TableFormer: Table Structure Understanding with Transformers

Author

Nassar, Ahmed, Livathinos, Nikolaos, Lysak, Maksym, and Staar, Peter

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a non-trivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-to-end deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.

Published
2022

24. Effect of cooling rate on aluminium-containing high-entropy alloys

Author

Nassar, Ahmed Elsayed Soliman, Mullis, Andrew, and Cochrane, Robert

Abstract

In this work, a HEA system is devised based on the outcome of neural network models. The most successful neural network in this work achieves a testing accuracy of 92%. This neural network operates solely on the compositional data of alloys, as opposed to the orthodox approach of using Hume-Rothery (HR) data. Considering that an alloy's composition is always known for certain (unlike HR features that are dependent on estimates), this approach is expected to enable the average researcher to rapidly screen potential HEA compositions. The outcome of the neural network model led to the study of the AlxCrCuFeNi system, whereby x = 1.4 was predicted to be the limit of the alloy's solid-solution window. The x = 1.0, x = 1.3, x = 1.5 and x = 2.0 compositions were manufactured using an arc-melter to confirm the prediction, whereby noticeable microstructural complexities are observed in the x = 1.5 system that are not observed in the x = 1.0 and x = 1.3 systems. 'Chinese Script' and 'Sunflower' structures are observed in the x = 1.5 system, whereas the x = 2.0 system displayed a microstructure dominated by intermetallics and very brittle mechanical behaviour. The x = 1.0 and x = 1.3 alloys showed Al-Ni intermetallic needles in their interdendritic regions which adhere closely to dendrite peripheries. The x = 1.0 alloy was processed for rapid solidification using a 6.5 m long drop-tube facility. This is in order to explore the possibility of suppressing intermetallic growth and achieving a single-phase simple solid solution. The sizes of the retrieved powders ranged from ˃ 850 µm to 38 µm, with a corresponding range of cooling rates from 112 K/s to 1.13×106 K/s. With higher cooling rates, simpler microstructures are obtained and at the highest cooling rate of around 1.13 ×〖10〗^6 K/s, a microstructure free of intermetallics is observed in powders of the 38 - 53 μm size fraction. The effect of rapid cooling is also studied in the eutectic HEA (EHEA) that is AlCoCrFeNi2.1. In equilibrium conditions, AlCoCrFeNi2.1 is dual-phase L12/B2. By processing AlCoCrFeNi2.1 using the drop-tube facility, rapidly-solidified powders were achieved with sizes from 850 µm ≤ d < 1000-38 µm ≤ d < 53 µm with corresponding estimated cooling rates of 114 K/s to 1.75×106 K/s respectively. Average interlamellar spacing was found to decrease from 2.10 µm in the as-cast alloy to 348 nm in the powders of the 38 µm < d < 53 µm size fraction. Although decreased interlamellar spacing is expected to enhance microhardness, such a relation was surprisingly not as strong as expected, as microhardness of the powders was found to vary only slightly from an average value of 340 Hv0.03. This unexpected result is explained via the observation of disorder trapping and increased FCC volume fraction. With increasing cooling rate, the microstructure of AlCoCrFeNi2.1 was found to evolve gradually from regular eutectic to colony eutectic, followed by dendritic with eutectic the interdendritic regions. In some instances at the highest cooling rate, dendritic structures may be observed with no eutectic observed in the interdendritic region. In particles of size d < 212 µm BCC dendrites were observed, either dominating the structure or coexisting with FCC dendrites.

Published
2022

25. Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm

Author

Koren, Mark, Nassar, Ahmed, and Kochenderfer, Mykel J.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Validating the safety of autonomous systems generally requires the use of high-fidelity simulators that adequately capture the variability of real-world scenarios. However, it is generally not feasible to exhaustively search the space of simulation scenarios for failures. Adaptive stress testing (AST) is a method that uses reinforcement learning to find the most likely failure of a system. AST with a deep reinforcement learning solver has been shown to be effective in finding failures across a range of different systems. This approach generally involves running many simulations, which can be very expensive when using a high-fidelity simulator. To improve efficiency, we present a method that first finds failures in a low-fidelity simulator. It then uses the backward algorithm, which trains a deep neural network policy using a single expert demonstration, to adapt the low-fidelity failures to high-fidelity. We have created a series of autonomous vehicle validation case studies that represent some of the ways low-fidelity and high-fidelity simulators can differ, such as time discretization. We demonstrate in a variety of case studies that this new AST approach is able to find failures with significantly fewer high-fidelity simulation steps than are needed when just running AST directly in high-fidelity. As a proof of concept, we also demonstrate AST on NVIDIA's DriveSim simulator, an industry state-of-the-art high-fidelity simulator for finding failures in autonomous vehicles., Comment: Accepted to IROS 2021

Published
2021

28. Robust PDF Document Conversion Using Recurrent Neural Networks

Author

Livathinos, Nikolaos, Berrospi, Cesar, Lysak, Maksym, Kuropiatnyk, Viktor, Nassar, Ahmed, Carvalho, Andre, Dolfi, Michele, Auer, Christoph, Dinkla, Kasper, and Staar, Peter

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval, I.7.5, I.5.1, I.5.2, I.5.4, I.5.5, I.2.1
Abstract

The number of published PDF documents has increased exponentially in recent decades. There is a growing need to make their rich content discoverable to information retrieval tools. In this paper, we present a novel approach to document structure recovery in PDF using recurrent neural networks to process the low-level PDF data representation directly, instead of relying on a visual re-interpretation of the rendered PDF page, as has been proposed in previous literature. We demonstrate how a sequence of PDF printing commands can be used as input into a neural network and how the network can learn to classify each printing command according to its structural function in the page. This approach has three advantages: First, it can distinguish among more fine-grained labels (typically 10-20 labels as opposed to 1-5 with visual methods), which results in a more accurate and detailed document structure resolution. Second, it can take into account the text flow across pages more naturally compared to visual methods because it can concatenate the printing commands of sequential pages. Last, our proposed method needs less memory and it is computationally less expensive than visual methods. This allows us to deploy such models in production environments at a much lower cost. Through extensive architectural search in combination with advanced feature engineering, we were able to implement a model that yields a weighted average F1 score of 97% across 17 distinct structural labels. The best model we achieved is currently served in production environments on our Corpus Conversion Service (CCS), which was presented at KDD18 (arXiv:1806.02284). This model enhances the capabilities of CCS significantly, as it eliminates the need for human annotated label ground-truth for every unseen document layout. This proved particularly useful when applied to a huge corpus of PDF articles related to COVID-19., Comment: 9 pages, 2 tables, 4 figures, uses aaai21.sty. Accepted at the "Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21)". Received the "IAAI-21 Innovative Application Award"

Published
2021

30. Modulatory mechanisms of copperII-albumin complex toward N-nitrosodiethylamine-induced neurotoxicity in mice via regulating oxidative damage, inflammatory, and apoptotic signaling pathways

Author

Shanab, Obeid, Mostafa, Laila, Abdeen, Ahmed, Atia, Rania, Nassar, Ahmed Y., Youssef, Mohammed, Ibrahim, Samah F., Maher, Zainab M., Imbrea, Florin, Fericean, Liana, Ghareeb, Khaled, Hasan, Tabinda, Ghamry, Heba I., Atawia, Reem T., Sadeq, Omar, and Abdelkader, Afaf

Published
2024
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31. GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end

Author

Nassar, Ahmed Samy, D'Aronco, Stefano, Lefèvre, Sébastien, and Wegner, Jan D.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object. Our method relies on a Graph Neural Network (GNN) to, detect all objects and output their geographic positions given images and approximate camera poses as input. Our GNN simultaneously models relative pose and image evidence, and is further able to deal with an arbitrary number of input views. Our method is robust to occlusion, with similar appearance of neighboring objects, and severe changes in viewpoints by jointly reasoning about visual image appearance and relative pose. Experimental evaluation on two challenging, large-scale datasets and comparison with state-of-the-art methods show significant and systematic improvements both in accuracy and efficiency, with 2-6% gain in detection and re-ID average precision as well as 8x reduction of training time.

Published
2020

35. Simultaneous multi-view instance detection with learned geometric soft-constraints

Author

Nassar, Ahmed Samy, Lefevre, Sebastien, and Wegner, Jan D.

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

We propose to jointly learn multi-view geometry and warping between views of the same object instances for robust cross-view object detection. What makes multi-view object instance detection difficult are strong changes in viewpoint, lighting conditions, high similarity of neighbouring objects, and strong variability in scale. By turning object detection and instance re-identification in different views into a joint learning task, we are able to incorporate both image appearance and geometric soft constraints into a single, multi-view detection process that is learnable end-to-end. We validate our method on a new, large data set of street-level panoramas of urban objects and show superior performance compared to various baselines. Our contribution is threefold: a large-scale, publicly available data set for multi-view instance detection and re-identification; an annotation tool custom-tailored for multi-view instance detection; and a novel, holistic multi-view instance detection and re-identification method that jointly models geometry and appearance across views., Comment: Internationcal Conference on Computer Vision 2019 (ICCV 19)

Published
2019

45. Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Author

Knight, Stephen R, Shaw, Catherine A, Pius, Riinu, Drake, Thomas M, Norman, Lisa, Ademuyiwa, Adesoji O, Adisa, Adewale O, Aguilera, Maria Lorena, Al-Saqqa, Sara W, Al-Slaibi, Ibrahim, Bhangu, Aneel, Biccard, Bruce M, Brocklehurst, Peter, Costas-Chavarri, Ainhoa, Chu, Kathryn, Dare, Anna, Elhadi, Muhammed, Fairfield, Cameron J, Fitzgerald, J Edward, Ghosh, Dhruv, Glasbey, James, van Berge Henegouwen, Mark I., Ingabire, J.C. Allen, Kingham, T Peter, Lapitan, Marie Carmela, Lawani, Ismaïl, Lieske, Bettina, Lilford, Richard, Martin, Janet, McLean, Kenneth A, Moore, Rachel, Morton, Dion, Nepogodiev, Dmitri, Ntirenganya, Faustin, Pata, Francesco, Pinkney, Thomas, Qureshi, Ahmad Uzair, Ramos-De la Medina, Antonio, Riad, Aya, Salem, Hosni Khairy, Simões, Joana, Spence, Richard, Smart, Neil, Tabiri, Stephen, Thomas, Hannah, Weiser, Thomas G, West, Malcolm, Whitaker, John, Harrison, Ewen M, Gjata, Arben, Modolo, Maria Marta, King, Sebastian, Chan, Erick, Nahar, Sayeda Nazmun, Waterman, Ade, Vervoort, Dominique, Bedada, Alemayehu Ginbo, De Azevedo, Bernardo, Figueiredo, Ana Gabriela, Sokolov, Manol, Barendegere, Venerand, Ekwen, Gerald, Agarwal, Arnav, Liu, Qinyang, Camilo Correa, Juan, Malemo, Kalisya Luc, Bake, Jacques, Mihanovic, Jakov, Kunčarová, Kamila, Orhalmi, Julius, Salem, Hosni, Teras, Jyri, Kechagias, Aristotelis, Arnaud, Alexis P, Lindert, Judith, Kalles, Vasileios, Aguilera-Arevalo, Maria-Lorena, Recinos, Gustavo, Baranyai, Zsolt, Kumar, Basant, Neelamraju Lakshmi, Harish, Zachariah, Sanoop Koshy, Alexander, Philip, Kumar Venkatappa, Sunil, Pramesh, C, Amandito, Radhian, Fleming, Christina, Ansaloni, Luca, Pellino, Gianluca, Altibi, Ahmed M., Nour, Ibrahim, Hamdun, Intisar, Ghellai, Ali M., Venskutonis, Donatas, Poskus, Tomas, Zilinskas, Justas, Malemia, Precious, Tew, Yong Yong, Borg, Elaine, Ellul, Sarah, Wafqui, fatima Zahraa, Borowski, David W, van Dalen, Anne Sophie, Wells, Cameron, Adamou, Harissou, Ademuyiwa, Adesoji, Adisa, Adewale, Søreide, Kjetil, Al Saqqa, Sara, Alser, Osaid, Tahboub, Haya, Segovia Lohse, Helmut Alfredo, Shu Yip, Sebastian, Major, Piotr, Sampaio Soares, António, Bratu, Matei Razvan, Litvin, Andrey, Vardanyan, Armen, Allen Ingabire, JC, Gudal, Ahmad, Albati, Naif, Juloski, Jovan, Rems, Miran, Rayne, Sarah, Van Straten, Stephanie, Moodley, Yoshan, Ortega Vázquez, Irene, Ruiz-Tovar, Jaime, Senanayake, Kithsiri Janakantha, Thalgaspitiya, Sujeewa Priyantha Bandara, Omer, Omer Abdelbagi, Homeida, Anmar, Cengiz, Yucel, Clerc, Daniel, Alshaar, Muhammad, Bouaziz, Hanen, Altinel, Yuksel, Doe, Matthew, Freigofer, Maryna, Teasdale, Ella, Kabariti, Rakan, Clements, Joshua Michael, Knight, Stephen Richard, Ashfaq, Ahsan, Azodo, Ijeoma, Wagner, Gabriela, Trostchansky, Ivan, Maimbo, Mayaba, Linyama, David, Nina, Helidon, Zeko, Amanda, Fermani, Claudio Gabriel, Villalobos, Santiago, Carballo, Federico, Farina, Pablo, Guckenheimer, Sebastian, Dickfos, Marilla, Ajmera, Ankit, Chong, Chester, Gourlay, Ralph, Hussaini, Sikandar, Lee, Yi Jia, Majid, Adeeb, Martin, Peter, Miles, Rebecca, Morris, Owen James, Phua, Jamie, Ridley, William, Saluja, Tarunpreet, Tan, Ryan Renxin, Teh, Jen, Wells, Anna, Arora, Bharti, Dollie, Qaasim, Ho, Debbie, Ma, Yanru, Perera, Omattage Mahasha, Truong, Anthony, Dawson, Amanda Caroline, Lim, Bryan, Pahalawatta, Upuli, Phan, Jacqueline, Woon-Shoo-Tong, Xiao-Ming Sarah, Yeoh, Andrea, Charman, Lillian, Drane, Andrew, Laura, Sharon, Lo, Charmaine Chu Wen, Mozes, Amy, Poon, Rita, Tan, Hao Han, Wall, Ellen, Chopra, Prakshi, De Giovanni, Jasmine, Dhital, Bal, Draganic, Brian, Duller, Alexander, Gani, Jonathan, Goh, Yao Kuan, Jeong, Jun Young, McManus, Brendan, Nagappan, Prakash, Pockney, Peter, Rugendyke, Anya, Sarrami, Mahsa, Smith, Stephen, Wills, Vanessa, Wong, Hsu Ven, Ye, Geoffrey, Zhang, Geoffrey, Brooker, Ethan, Feng, Daniel, Lau, Bonnie, Ngai, Carlin, Birks, Sarah, Gyorki, David, Otero de Pablos, Jaime, Abbosh, Ali, Gillespie, Chris, Mahmoud, Ahmed, Kwan, Bianca, Lawson, Joshua, Warwick, Andrea, Bingham, Janne, Cockbain, Andrew J, Dudi-Venkata, Nagendra Naidu, Ellaby-Hall, Jordan, Finlay, Ben, Humphries, Emily, Pisaniello, Jade, Pisaniello, Monique, Salih, Salma, Sammour, Tarik, Abd Wahab, Haidar Hadri, De Silva, April, Hayward, Nicola, Iyer, Kartik, Maddern, Guy, Prevost, Gian Andrea, Annapureddy, Naga, Settipalli, Krishna Pranathi, Yeo, Jeremy, Hempenstall, Lucy, Pham, Lily, Purcell, Shaun, Talavera, Cherry, Vaska, Ashish I, Chaggar, Gurpreet, Chrapko, Phillip, Cocco, Annelise, Coulter-Nile, Sarah Michelle Crystal Jade, Ctercteko, Grahame, French, James, Gong, Houchen, Gosselink, Martijn, Jegathees, Thuvarahan, Jin, Ivan, Kalachov, Michelle, Kiefhaber, Kathryn, Lee, Katherine, Luong, Jason, Phan, Steven, Pleass, Henry, Veale, Kelly, Zeng, Zhi, Au, Angela, DeBiasio, Ashe, Deng, Idy, Myooran, Jananee, Nair, Amrita, Stewart, Peter, Stift, Anton, Unger, Lukas Walter, Wimmer, Kerstin, Ahmed, Nabila, Hasan, Syed, Rahman, Saber, O'Shea, Margaret, Padmore, Greg, Peters, Adrian, Perduca, Pietro, Pulcina, Guenda, Tinton, Nicolas, Buxant, Frederic, Dabin, Elsa, Garofalo, Giulia, Dossou, Francis, Gnangnon, Freddy Houehanou Rodrigue, Imorou Souaibou, Yacoubou, Motlaleselelo, Pako, Tlhomelang, Omphile, Lima Buarque, Igor, Mendonça Ataíde Gomes, Gustavo, Vieira Barros, Aldo, Batashki, Ilia, Damianov, Nikolai, Stoyanov, Vladislav, Dardanov, Dragomir, Maslyankov, Svilen, Petkov, Plamen, Todorov, George, Zhivkov, Evgeni, Akisheva, Aygulya, Castilla Moreno, Miguel Angel, Genov, Geno, Ilieva, Ivelina, Ivanov, Tsvetomir, Karamanliev, Martin, Khan, Azhar, Mitkov, Emil, Yotsov, Tsanko, Atanasov, Boyko, Belev, Nikolay, Slavchev, Mihail, Nsengiyumva, Carlos, Jones, Elgan, Stock, Simon, Kyota, Steve, Brown, James, Mabanza K., Tresor, Nigo Samuel, Lemery, Otuneme, Chidi, Prosper, Ngwang, Umenze, Franklin, Boutros, Marylise, Caminsky, Natasha, Dumitra, Sinziana, Garfinkle, Richard, Morency, Dominique, Salama, Ebram, Banks, Alexander, Ferri, Lorenzo, He, Haitian, Katz, Amit, Liberman, Alexander Sender, Meterissian, Sarkis, Pang, Allison, Parvez, Elena, Hameed, Usmaan, Osman, Fahima, Sequeira, Sangita, Coburn, Natalie, Jaffer, Alisha, Karanicolas, Paul, Mosseler, Matthew, Musselman, Reilly, Liu, Xinyuan, Yip, Ching Wan, Garces-Otero, Juan Sebastian, Guzman, Carolina, Sierra, Sebastian, Uribe Valencia, Andres, Cabrera Rivera, Paulo Andrés, Camelo, Saul, Gonzalez, Andrea, González-Orozco, Alejandro, Mosquera Paz, Manuel Santiago, Perez Rivera, Carlos J, Gonzalez, Felipe, Isaza-Restrepo, Andres, Nino- Torres, Laura, Arias Madrid, Natalia, Mendoza Arango, Maria Clara, Tsandiraki, Justin, Jemendžić, Damir, Kocman, Branislav, Šuman, Oliver, Canic, Renata, Jurišić, Darko, Karakas, Ivana, Krizanovic Rupcic, Ana, Pitlovic, Vlatka, Samardžić, Josip, Kopljar, Mario, Bacic, Ivan, Domini, Edgar, Karlo, Robert, Miljanić, Danijela, Simic, Andrea, Ahmed, Mariam, Al Nassrallah, Majdi, Altaf, Rabiya, Amjad, Talal, Eltoum, Ruba, Haidar, Heba, Hassan, Alhassan, Khalil, Omar, Qasem, Marwan, Ramesh, Rommel, Sajith, Gautham, Wisal, Maham, Žatecký, Jan, Bujda, Michele, Jirankova, Katerina, Paclik, Ales, Abdallah, Aya, Abdulgawad Almogy, Mariam, Ayman El-sawy, Esraa, ElFayoumy, Ahmed Moustafa, Elghareeb, Nourhan, Esmat, Nourhan Ahmed, Fadel, Ahmed, Habater, Abdullah, Hamdy, Heba, Hefni, Amr, Kamal, Marwa, Mohamed Abobakr, Norhan, Sayed, Ahmed, Shaker, Nancy, Taha, Ehab, Tharwat, Hoda, Zakaria, Omar, Abdelmotaleb, Ibrahem, Al-Dhufri, Ali, Al-Himyari, Hamza S., El sheikh, Enas, Eldmaty, Asmaa, Elkhalawy, Aya, M.Elkhashen, Ahmed, Magdy, Kithara, Mostafa, Safa, Sadia, Habib Doutoum, Saleh, Mohamed mahmoud, Samir, Dina, Yahia Mohamed Ali, Mohamed, A. Nassar, Mahmoud, Abdelhady, Samar, Abdelrazek, Aly, Abdelsalam, Israa, El-Sawy, Aya, Essam, Eman, Gadelkarim, Mohamed, Ghaly, Khaled, Hassabalnaby, Mohamed, Masarani, Rana, Mohamed Shaaban, Nourhan, Sabry, Ahmed, Salem, Menatalla, Soliman, Nourhan Akram, Zahran, Diaaaldin, Abou El.soud, Moustafa Ramadan, Badr, Esraa Tarek, Borham, Hala, Elmeslemany, Nehal, Elsayed, Mohammad, Elsherif, Fawzia, Eslam, Sara, Gaber, Gehad, Ibrahim, Sondos, Kamh, Yara, Mahmoud, Abdelrahman, Mohamed, Shimaa gamal, Morshedy, Eman, Omar, Cinderella, Salem Soliman, Fatima, Abdelkawy, Shaza, Abdelmohsen, Naglaa, Abdelshakour, Mahmoud, Dahy, Ahmed, Gamal, Norhan, Gamal, Mohammed, Hasan, Ahmad, Hetta, Helal, Mousa, Nehad, Omar, Mohamed, Rabie, Somia, Saad, Mahmoud, Saleh, Bakeer, Sayed Mohamed, Marwa, Shawqi, Muhammad, Abdelhady Mousa, Heba, Alnoury, Mostafa, Elbealawy, Mohamed, Elshafey, Ahmed, Essam Ibrahim El Desouki Muhammad Ahmed, Muhammad, Ghonaim, Mennatullah, Hgag, Fawzy, Ibrahim, Mohamed, Morsy, Mahmoud, Reda Loaloa, Mohamed, Refaat, Ahmed, Samir, Hadeer, Shahien, Fatma, Sobhy, Mohamed, Sroor, Fathy, Abdellatif, Esraa, Adel, Marina, Afifi, Amr Abdelghani, Afifi, Eman, Antaky, Marco, Dawoud, Amr, El Zoghby, Naira, El-remaily, Amira, Elzanfaly, Ali Abdelazez, Gadallah, Ahmed, Gamal, Fatma Alzahraa, Hashem, Omar, Medhat Youssef, Shrouk, Muhammad Attyah, Aliaa, Munir, Malak, Shazly, Omar, Taha, Esraa, Wilson, Karim, Adel, Sawsan, Ali, Asmaa, Eid, Esraa, Elhelow, Esraa, Elmahdy, Marwa, Elshatby, Bassant, Hossam el-din Zakaria, Amany, Hossny, Ahmad, Ibrahim, Eman, M.Yonis, Ahmed, Metwalli, Maram, Yousry, Basant, Zid, Esraa, A Yacoub, Mina, Abdelhakim, Ahmed, Abouelsoad, Nervana, Alkhatib, Mo'min, Ashraf, Ahmed, Ashraf, Alaa, Elazab, Yasmin, Elfanty, Mahmoud, Elkabir, Osama, Elsayed, Mai, Elshimy, Ahmed, Elsobky, Hager, Eskander, John, Gad, Ahmed, Hamsho, Ward, Khaled Abdelwahed, Noura, Magdy, Menna, Moharam, Dalia, Osama, Abeer, Ramadan, Shereen, Roum, Radwa, Sayed, Taqwa, Shehada, Tarneem, Zidan, Ahmed Mohy, Abbas, Khalid, Ali, Amr, Attia, Mohamed, Balata, Mohamed, El Nakeeb, Ayman, Elewaily, Mohamed Ibrahim Elsayed, Elfallal, Ahmed, Elfeki, Hossam, Elkhadragy, Ahmed, Emile, Sameh, Ezzat, Helmy, Hosni, Hasnaa, Mansour, Islam, Omar, Waleed, Othman, Gehad, Sadek, Kareem, Shalaby, Mostafa, Shehab-Eldeen, Noura, Anas khalifa, Rawda, Badr, Helmy, Eldeep, Mostafa, Eldeep, Ahmed, Eldoseuky mohammed, Amany, Khallaf, Salwa, Magdy Hegazy, Eman, Mahmoud, Rokia, Mikhail, Pola, Morsi, Mahmoud, Mowafy, Sara, Raafat, Dina, Safy, Amina, Sera, Marwa, Sera, Ahmed shible, AbdAllah, Mostafa Salim Mohamed, Abdelkader, Muhammad, Abdou, Abdulrahman Osama, Ahmed, Ahmedgaber, Gaafar, Shireen, Ibrahim negm, Fatma, Lapic, Mina, Maher, Ahmed, Mahmoud, Hagar, Mostafa, Ahmed, Samir, Mohamed, Samy, Fatma, Semeda, Nourhan, Shalaby, Hind I., El-taweel, Alaa, Galal Elnagar, Ahmed, Hemidan, Ahmed Gamal, Hussein, Mohamed, Kandil, Ahmed.A., Moawad, Mf, Nasser Hamamah, Ayat Allah, Soliman, Mostafa, Abdelkhalek, Mohamed, Abdelmaksoud Tawakel, Noura, Abdelwahed, Ahmed Mohamed, Abdou, Alrawy, Atallah, Khalid, Elsherbeny, Mohammed Yasser, Emara, Eman, Hamdy, Mohamed, Hamdy, Omar, Haron, Amira, Ismail, Salma, Metwally, Islam Hany, Mohamed Hamed Elgaml, Nihal, Nassar, Ahmed, Refky, Basel, Sadek, Mirna, Saleh, Mahmoud, Yunes, Asmaa, Zakaria, Mai, Zuhdy, Mohammed, Fayed, Notila, Mohammed, Mohammed Mustafa Hassan, Kütner, Sander, Melnik, Priit, Seire, Indrek, Ümarik, Toomas, Ainoa, Eppu, Eerola, Verner, Koppatz, Hanna, Koskenvuo, Laura, Sallinen, Ville, Takala, Sini, Katunin, Jevgeni, Turunen, Arto, Christou, Niki, Mathonnet, Muriel, Lavoue, Vincent, Nyangoh Timoh, Krystel, Soulabaille, Lucie, Lesourd, Romain, Merdrignac, Aude, Sulpice, Laurent, André, Benoît, Chantalat, Elodie, Vaysse, Charlotte, Dousset, Bertrand, Gaujoux, Sebastien, Martin, Gregory, Clonda, Octavian, Juodis, Domantas, Kienle, Klaus, Mravik, Andras, Palmer, Samuel, Szabadhegyi, Gabor, Agbeko, Anita Eseenam, Gyabaah, Solomon, Gyamfi, Frank Enoch, Naabo, Nuhu, Owusu senior, Atta, Yorke, Joseph, Owusu, Frank, Abantanga, Francis, Anyomih, Theophilus Teddy Kojo, Muntaka, Abdul-Jalilu Mohammed, Owusu Abem, Emmanuel, Sheriff, Mohammed, Wondoh, Paul M., Balalis, Dimitrios, Korkolis, Dimitrios, Gkiokas, Georgios, Pantiora, Eirini, Theodosopoulos, Theodosios, Ioannidis, Argyrios, Konstantinidis, Konstantinos, Konstantinidou, Sofia, Machairas, Nikolaos, Paspala, Anna, Prodromidou, Anastasia, Chouliaras, Christos, Papadopoulos, Konstantinos, Baloyiannis, Ioannis, Mamaloudis, Ioannis, Tzovaras, George, Akrida, Ioanna, Argentou, Maria-Ioanna, Germanos, Stylianos, Iliopoulos, Evangelos, Maroulis, Ioannis, Skroubis, George, Theofanis, George, Chatzakis, Christos, Ioannidis, Orestis, Loutzidou, Lydia, Karathanasis, Panagiotis, Michalopoulos, Nikolaos, Theodoropoulos, Charalampos, Theodorou, Dimitrios, Triantafyllou, Tania, Garoufalia, Zoe, Hasemaki, Natasha, Kontos, Michalis, Kouraklis, Gregory, Kykalos, Stylianos, Liakakos, Theodore, Mpaili, Eustratia, Papalampros, Alexandros, Schizas, Dimitrios, Syllaios, Athanasios, Tampaki, Ekaterini Christina, Tsimpoukelis, Antonios, Antonopoulou, Maria Ioanna, Deskou, Eirini, Manatakis, Dimitrios K., Papageorgiou, Dimitrios, Zoulamoglou, Menelaos, Anthoulakis, Christos, Margaritis, Michalis, Nikoloudis, Nikolaos, Campo, Veronica, Ceballos, André, Flores, Mario-Andrés, Giron, Waleska, Ko, Donghyun, Martinez, Gabriel, Rivera Lara, Verónica, Rueda, Nataly, Sanchez, Andres, Tejeda Garrido, Jorge Carlos Guillermo, Alvarez Rivera, Alvaro Eduardo, Bamaca Ixcajoc, Elvis Benjamin, Barreda Zelaya, Lilian Elizabeth, Chacòn-Herrera, Patricia, Corea Ruiz, Ligia Margarita, Echeverria-Davila, Guillermo, Garcia, Mario, García, Danilo, Gutiérrez Mayen, Edgar Fernando, José, Noriega, Mazariegos, Nery, Méndez, Diego, Paniagua Espinoza, Michael, Bardos, David, Benke, Marton, Illes, Kristof, Kokas, Balint András, Szabó, Réka, Appukuttan, Akhila, Asok, Anjitha, D.k, Vijaykumar, Malik, Kapil, Ravishankaran, Praveen, Tapkire, Ritesh, Moorthy, Guru, Abraham, Joyner, Muthuvel, Ramesh, Alapatt, John, Kattepur, Abhay, Pareekutty, Nizamudheen, Garod, Mebanshanbor, Harris, Caleb, Wanniang, Cliff, Gupta, Ashish, Nehra, Deepak, Parshad, Sanjeev, Acharya, Rajgopal, Badwe, Rajendra, Bhandare, Manish, Jain, Urvashi, Kirti, Karishma, Nair, Nita, Shrikhande, Shailesh, Thakkar, Purvi, Anandan, Premkumar, C S, Archana, Holenarasipur Narasannaiah, Arun, Jagarlamudi, Tejaswi, M R, Rashmi, Manangi, Mallikarjuna, Raghavendra, Abhishek, Rao, K. Seshagiri, S, Vinay, Sajjan, Vinay, Shenoy, Aneesh, Shivashankar Chikkanayakanahalli, Santhosh, Tharanath, Kavya, V, Sushmita, Adidharma, Peter, Agarwal, Raksheeth, Anggita Gultom, Phebe, Arifin, Ghafur Rasyid, Billy, Matthew, Elfizri, Zatira, Fahira, Alessa, Felicia, Devi, Gunardi, Triana Hardianti, Johanna, Nadya, Nugrahadi, Nadia Rahmadiani, Panigoro, Sonar Soni, Rahmayanti, Siti, Sihotang, Retta Catherina, Brata, Santi Yuanita, Winoto, Hadi, Barati, Nastaran, Karami, Manoochehr, Khorshidi, Hamidreza, Naderifar, Homa, Abdulla, Mazin A., Coleman, Maggie, Doherty, Ronan J, Hannon, Rob, Murphy, Brenda, Stakelum, Aine, Winter, Des, Aljohmani, Lylas, Farnan, Richard, Seldon, Yeshey, Tan, Tanna, Varghese, Shriya, Alherz, Mohammad, Ather, Muaaz, Bajilan, Mohammad, Graziadei, Vivien, Pilkington, Isobel, Quidwai, Omar, Ridgway, Paul, Shiwani, Haaris, Tahir, Abd al-Rahman, Blunnie, Eimear, Burke, Daniel, Kennedy, Niall, Macdonagh, Kate, O'Neill, Maeve, Rooney, Siobhan, Falco, Giuseppe, Ferrari, Guglielmo, Mele, Simone, Nita, Gabriela Elisa, Ugoletti, Lara, Zizzo, Maurizio, Confalonieri, Gianmaria, Pesenti, Giovanni, Tagliabue, Fulvio, Baronio, Gianluca, Ongaro, Deborah, Pata, Giacomo, Compagnoni, Bruno, Salvadori, Renato, Taglietti, Lucio, D'Alessandro, Nicola, Di Lascio, Pierpaolo, Pascale, Giovanni, Bortolasi, Luca, Campagnaro, Tommaso, Carlini, Massimo, Lisi, Giorgio, Lombardi, Davide, Pedrazzani, Corrado, Spoletini, Domenico, Turri, Giulia, Violi, Paola, Altomare, Donato Francesco, Aquilino, Fabrizio, Musa, Nicola, Papagni, Vincenzo, Picciariello, Arcangelo, Vincenti, Leonardo, Andreotti, Dario, Occhionorelli, Savino, Tondo, Matteo, Basso, Stefano Maria Massimiliano., Ubiali, Paolo, Cirelli, Riccardo, Maino, Marco Enrico Mario, Piozzi, Guglielmo Niccolò, Picone, Emanuele, Scaramuzzo, Rosa, Sinibaldi, Giovanni, Amendola, Alfonso, Anastasio, Lorenzo, Bucci, Luigi, Caruso, Emanuele, Castaldi, Antonio, Di Maso, Sara, Dinuzzi, Vincenza Paola, Esposito, Giovanni, Gaudiello, Maria, Giglio, Mariano Cesare, Greco, Paola Antonella, Luglio, Gaetano, Manfreda, Andrea, Marra, Ester, Mastella, Federica, Pagano, Gianluca, Peltrini, Roberto, Pepe, Vincenzo, Sacco, Michele, Sollazzo, Viviana, Spiezio, Giovanni, Cianchetti, Ettore, Menduni, Nunzia, Carvello, Michele Maria, Di Candido, Francesca, Spinelli, Antonino, Corsi, Fabio, Sorrentino, Luca, Marino, Fabio, Asti, Emanuele Luigi Giuseppe, Bonavina, Luigi, Rausa, Emanuele, Asta, Martina, Belli, Andrea, Bianco, Francesco, Cervone, Carmela, Delrio, Paolo, Falato, Armando, Fares Bucci, Andrea, Guarino, Rita, Pace, Ugo, Rega, Daniela, De Luca, Emilia, Gallo, Gaetano, Sammarco, Giuseppe, Sena, Giuseppe, Vescio, Giuseppina, Santandrea, Letizia, Ugolini, Giampaolo, Zattoni, Davide, Chetta, Nicola, Logrieco, Gaetano, Vanella, Serafino, Garulli, Gianluca, Zanini, Nicola, Bondurri, Andrea, Cammarata, Francesco, Colombo, Francesco, Foschi, Diego, Lamperti, Giulia Maria Beatrice, Maffioli, Anna, Sampietro, Gianluca Matteo, Yakushkina, Al'ona, Zaffaroni, Gloria, Cicuttin, Enrico, Sibilla, Maria Grazia, Impellizzeri, Harmony, Inama, Marco, Moretto, Gianluigi, Mochet, Sylvie, Ponte, Elisa, Usai, Antonella, Mancini, Stefano, Sagnotta, Andrea, Solinas, Luigi, Bolzonaro, Elisa, Tamini, Nicolò, Curletti, Gianluca, Galleano, Raffaele, Malerba, Michele, Campanella, Sofia, Cocorullo, Gianfranco, Colli, Francesco, De Marco, Paolino, Falco, Nicolò, Fontana, Tommaso, Kamdem Mambou, Leonel jospin, La Brocca, Antonella, Licari, Leo, Randisi, Brenda, Rizzo, Giovanna, Rotolo, Giulia, Salamone, Giuseppe, Tutino, Roberta, Venturelli, Paolina, Malabarba, Stefano, Sgrò, Alessandro, Vella, Ivan, Cirillo, Bruno, Crocetti, Daniele, De Toma, Giorgio, Lapolla, Pierfrancesco, Mingoli, Andrea, Sapienza, Paolo, Belvedere, Angela, Bianchini, Stefania, Binetti, Margherita, Birindelli, Arianna, Tonini, Valeria, Podda, Mauro, Pulighe, Fabio, De Rosa, Michele, Bono, Lorenzo, Borghi, Felice, Geretto, Paolo, Giuffrida, Maria Carmela, Lauro, Corrado, Marano, Alessandra, Pellegrino, Luca, Salusso, Paola, Sasia, Diego, Campanelli, Michela, Realis Luc, Alberto, Trompetto, Mario, Cardia, Roberto, Cillara, Nicola, Giordano, Antonio Nicola, Costanzo, Antonio, Giovilli, Mario Alessandro, Turati, Luca, Canonico, Silvestro, Sciaudone, Guido, Selvaggi, Francesco, Selvaggi, Lucio, Albsoul, Nader, AlBsoul, Ahmad, Alkhatib, Ala'a Aldeen, Alsallaq, Osama, Amarin, Justin Z., Ayoub, Rami, Bsisu, Isam, El Muhtaseb, M S, Jabaiti, Mohammad, Melhem, Jamal, Qwaider, Yasmeen Z., Salameh, Mohammad Hasan, Suleihat, Ahmad, Suradi, Haya H., Alammarin, Mohammad, Aljaafreh, Almoutuz, Bani hani, Mohammad, Bani hani, Zeina, Bani Hani, Farah, Fahmawee, Toqa, Hamouri, Shadi, Katanani, Cyrine, Tawalbeh, Ra'fat, Tawalbeh, Tamara, Zawahrah, Hassan, Abou Chaar, Mohamad K., Abusalem, Lana, Al-Masri, Mahmoud, Al-Najjar, Hani, Barghuthi, Lutfi, Ahmed, Zahra, Maulana, Adnan, Ngotho, Omar, Kamau, Charbel, Stanley Mwenda, Aruyaru, Bosire, Fridah, Mwachiro, Elizabeth, Parker, Robert, Simel, Ian, Sylvester, Kimutai, Althini, Abdulmunem Ahmed Mustafa, Elbarouni, Sofian, Elbeshina, Aya Elseed, Gwea, Ahmed, Malek, Ans, Masoud Farag, Wedad Albashir, Abdalei, Abdulwahab, Abdel Malik, Abu Baker, Abo-khammash, Areej, Abuhlaiga, Ma'aly, Adnan, Nour, Albaggar, Marwa, Alfitory, Asma, Aljanfi, Asma, Almuzghi, Fakhruddin, Altumei, Zohoor, Alzabti, Fatima, Ashoushan, Hana, Assalhi, Mohamed, Azzubia, Joma, Bnhameida, Sondos, Delhen, Malik, Elshafei, Houssein, Elteir, Hana, Esbaga, Fatima, Gobbi, Abdel Aziz, Hamouda, Fatma, Hilan, Hamdan, Ismail, Rania, Jebran, Fieruz, Kasbour, Muataz, Maderi, Galia, Mohammad, Saja, Mohammed, Burooj, Murtadi, Habib, Mustafa, Hamassat, Rajab, Mohamed, Trenba, Sarah, Wafaa, Mariam, Al Sagheir, Eman, Almigheerbi, Alabas, Alzahaf, Ahmed, Bahroun, Sumayyah Ghayth, Ben Dallah, Najah, Elshaibani, Mahmoud, Eswaye, Haitem, Karar, Maha, Omar, Samah, Younes, Eman, Younes, Maha, Zreeg, Dafer, Abujamra, Saleh, Ashour, Firas, Elgammudi, Mala, Omar F. Aljadidi, Wesal, Saddouh, Enas, Sharif, Randa, Alabuzidi, Aya, Alwerfally, AbdulMawlay, Aribi, Sarra, Bibas, Fatma, Elfaituri, Taha, Elhajjaji, Yasmine, Khaled, Ala, Khalil, Wegdan, Layas, Tesneem, Soula, Enas, Tarek, Ahmed, Abu hallalah, Muad fathi khalleefah, Ahmed, Hazem Abdelkarem, Alsharef, Tagwa, Ben Saoud, Abdulsalam Ali, El Gharmoul, Tasnim, Elhadi, Ahmed, Elrais, Safa, Shebani, Abdulhalim, Zarti, Heba, Zeiton, Asaid, Ambrazevicius, Marijus, Kaselis, Nerijus, Stakyte, Migle, Aliosin, Oleg, Cizauskaite, Agne, Dailidenas, Sarunas, Eismontas, Vitalijus, Kybransiene, Migle, Nutautiene, Vitalija, Samalavicius, Narimantas, Simcikas, Dainius, Slepavicius, Algirdas, Tamosiunas, Albinas, Ubartas, Nerijus, Zeromskas, Paulius, Bradulskis, Saulius, Dainius, Edvinas, Juočas, Juozas, Kubiliute, Egle, Kutkevičius, Juozas, Opolskis, Aurimas, Parseliunas, Audrius, Subocius, Andrejus, Virbickaite, Egle, Zuikyte, Diana, Bogusevicius, Algirdas, Buzaite, Kristina, Čepuliené, Daiva, Cesleviciene, Ieva, Cesna, Vaidotas, Gribauskaite, Jolanta, Ignatavicius, Povilas, Jokubauskas, Mantas, Liugailaitè, Monika, Margelis, Ernest, Mazelyte, Ruta, Pankratjevaitè, Lina, Pažusis, Matas, Rackeviciute, Agne, Saladyte, Justina, Škimelytè, Monika, Šlenfuktas, Vygintas, Sudeikyte, Monika, Tamelis, Algimantas, Vanagas, Tomas, Žumbakys, Žygimantas, Atkociunas, Aivaras, Dulskas, Audrius, Kuliavas, Justas, Birutis, Justas, Paškevičius, Sigitas, Šatkauskas, Mindaugas, Danys, Donatas, Jakubauskas, Matas, Jakubauskiene, Lina, Kryzauskas, Marius, Lipnickas, Vytautas, Makūnaitè, Gabija, Rasoaherinomenjanahary, Fanjandrainy, Rasolofonarivo, Herizo, Samison, Luc Hervé, Banda, Bitiel, Msosa, Vanessa, Ahmad Izzuddin, Ahmad Imran, Das, Andre, Gan, Ying Yee, Shong Sheng, Tan, Siaw, Jia yng, Ab Rahim, Mohd Fadliyazid, Abang Jamari, Dyg Zahratul Hamrak, Che Husin, Nurfariza, Kamarulzaman, Muhd Yusairi, Lim, Yi Ping, Mohamed Kamil, Nil Amri, Mohd Hassan, Mohd Razeen, Mohd Sahid, Saidah, Mustafa, Johari, Ng, Elaine Hui Been, Wan Khazim, Wan Khamizar, Chang Ern, Ng, Lingeshan, P.g., Sulaiman, Syariz Ezuan, Ang, Sue Ean, Bin Mohamad Sithik, Muhammad Navid, Cheong, Yih Jeng, Deva Tata, Mahadevan, Jia Xian, Law, Kadravello, Aravinthan, Koh, I-Ern, Ng, Li-Yen, Ng We Yong, Yuki Julius, Palayan, Kandasami, Sam, Chi Xuan, Siow Jin, Phuah, Tan Ern Hwei, Jeremy, Tang, Yita, Ter, Alvin Zubin, Wong, Michael Pak-Kai, Zakaria, Andee Dzulkarnaen, Zakaria, Zaidi, Henry, Fitjerald, Kalaiselvan, Thyivya, Abd Karim, Muhammad Fairuz Shah, Abdul Aziz, Mohamed Rezal, Abdul Aziz, Nora, Khong, Tak Loon, Lau, Peng Choong, Lim, Hiong Chin, Roslani, April Camilla, Seak, Jonathan Chen Ken, Wong, Sui-Weng, Wong, Lai Fen, Yeen Chin, Leow, Anyanwu, Mercy Chinemerem, Busuttil, Zachary, Calleja, Thomas, Chircop, Kurt Lee, Cutajar, Ruth, Dimech, Andrew Michael, Galea, Joseph, Gascon Perai, Kiara, Gatt, Ruth, Kelman, Lisa, Micallef, Elizabeth, Nwolu, Favour, Sammut, Kim, Thompson, Joanna, Warwicker, Sean, Zammit, Matthew, Cordera, Fernando, Cruz González, Efraín, Sánchez-García, Jorge, Barbosa Camacho, Francisco José, Barrera López, Francisco Javier, Zuloaga Fernandez del Valle, Carlos Jose, Acosta, Eric, González Espinoza, Iván Romarico, Moreno, Perla, Cortes-Flores, Ana Olivia, Fuentes Orozco, Clotilde, Gonzalez Ojeda, Alejandro, Corro Díaz González, Samantha, Martinez, Laura, Mosqueda Amador, Bonifacio, Novoa, Armando, Olazo Espejo, Dennet Arturo, Jimenez, Alejandro, Lopez Rosales, Federico, Vanoye, Elva Gabriela, Garcia Gonzalez, Luis Alberto, Miranda-Ackerman, Roberto Carlos, Solano-Genesta, Manuel, Alvarez-Cano, Alethia, Romero-Garza, Hector Hugo, Medina-Franco, Heriberto, Mejía-Fernández, Lorelí, Salgado-Nesme, Noel, Vergara-Fernandez, Omar, Gutiérrez-Mota, Guadalupe Montserrat, Hernandez Vera, Francisco Xavier, Llantada Lopez, Anabella, Morgan Villela, Gilberto, Ramirez Padilla, Felipe de Jesus, Tapia Marin, Walezka, Martínez Maldonado, Mónica, Sánchez Suárez, Ramses, Troche, José Manuel, Benyaiche, Chaymae, Outani, Oumaima, Amine, Souadka, Benkabbou, Amine, Majbar, Anass Mohammed, Mohsine, Raouf, Rafik, Ali, Oung, Thida, Tin, Moe Moe, Plarre, Philipp, Alberga, Anna, Sluiter, Nina, Tuynman, Jurriaan, Blok, Robin, Cömert, Didem, Hompes, Roel, Kalff, Marianne, Stellingwerf, Merel Elisabeth, Tanis, Pieter, van Berge Henegouwen, Mark, van Praag, Elise Maria, Wisselink, Daan, Gerhards, Michael, Lopes Cardozo, Josephine, Westerduin, Emma, de Jonge, Joske, van Geloven, Aaw, van Schilt, Kaz, den Boer, Frank, Stoots, Simone, Vlek, Stijn, Adams, Jamie, Al-Busaidi, Ibrahim S., Budd, Gabrielle, Choi, Seung il, Chu, Michael Jen Jie, Ganugapati, Anurag, McKinstry, Lucy, Pascoe, Rebecca, Richards, Simon, Rosser, Kenrick, Stevenson, Annie, White, Rebecca, Farik, Shebani, Kwun, Jin, Murad, Ahmed, Cowan, Sarah, Hall, Timothy, Hayton, Michael, Malam Sani, Laminou, Oumarou Garba, Souleymane, Amadou Magagi, Ibrahim, Habou, Oumarou, Aliyu, Halima, Daniyan, Muhammad, Sholadoye, Tunde T., Abdullahi, Lawal, Anyanwu, Lofty-John, Mohammad Mohammad, Aminu, Muhammad, Abubakar Bala, Sheshe, Abdurrahman Abba, Suleiman, Ibrahim, Adesina, Alaba, Awolowo, Ajibola, Onuoha, Clement, Salami, Omotayo, Taiwo, Ogechukwu, Taiwo, Agboola, Kache, Stephen, Makama, Jerry Godfrey, Sale, Danjuma, Abiola, Olajide, Ajao, Akinlabi, Ajiboye, Anthony, Etonyeaku, Amarachukwu, Olaogun, Julius, Adebanjo, Ademola, Adesanya, Opeoluwa, Afolayan, Michael Olatunji, Balogun, Olanrewaju, Makanjuola, Ayomide, Nwokocha, Samuel, Ojewola, Rufus Wale, Olajide, Thomas Olagboyega, Aderounmu, Adewale, Adesunkanmi, Abdul-Rashid, Agbakwuru, Augustine, Akeem Aderogba, Adeleke, Alatise, Olusegun Isaac, Arowolo, Olukayode, Lawal, Oladejo, Mohammed, Tajudeen, Ndegbu, Chinedu, Olasehinde, Olalekan, Wuraola, Funmilola, Akinkuolie, Akinbolaji, Mosanya, Arinzechukwu, Ayandipo, Omobolaji, Elemile, Peter, Lawal, Taiwo Akeem, Ali SANI, Samuel, Garba, Stephen, Hauwa SANI, Rebecca, Olori, Samson, Onyebuashi, Henry, Umoke, Ifeanyi, Adenuga, Adedire, Adeyeye, Ademola, Habeeb, Olufemi, Lawal, Bashir, Nasir, Abdulrasheed, Aahlin, Eirik Kjus, Kjønås, Didrik, Myrseth, Elisabeth, Abbasy, Jibran, Alvi, Abdul, Saleem, Omair, Afzal, Asma, Nazir, Anam, Farooq, Muhammad, Liaqat, Ayesha, Naqi, Syed Asghar, Raza, Ali, Sarfraz, Muzna, Sarwar, Muhammad, Banglani, Muntaha, Munir, Ambreen, Sehrish, Rahmat, Ayub, Bushra, Sayyed, Raza, Altaf, Amna, Ayub, Saima, Saeed, Komal, Syed, Bilal, Akbar, Sana Amir, Anwer, Abdul Wahid, Khan, Ruqayya Naheed, Khan, Amina Iqbal, Khattak, Shahid, Mohtasham, Sameen, Parvaiz, Muhammad Asad, Syed, Aamir Ali, Ansari, Abdul Basit, Shahzad, Noman, Khaliq, Tanwir, Rashid, Isbah, Waqar, Shahzad Hussain, Abu Al-saleem, Hasan, Abu Alqumboz, Amjad, Alqadi, Mohammad, Amro, Adham, Assa, Rawan, Awesat, Eman, Ayyad, Rawan, Hammad, Mohammed, Haymony, Ayat, Hijazi, Bassel, Hmeidat, Bara, Lahaseh, Rowaa, Qawasmi, Aseel, Rajabi, Alaa, Shehada, Mohammed, Shkokani, Sundus, Yaghi, Yasmine, Yaghi, Nadine, AlZohour, Mohammad, Farid, Mohammad, Habes, Yousef Mahmoud, Juba, Wesam, Nubani, Yanal, Rabee, Abdelrahman, Sa'deh, Mohammad, Abed, Saeed, Al basos, Iyad, Alswerki, Mohammad, Ashour, Dina, Awad, Israa, Diab, Samar, El Jamassi, Alaa, El-Kahlout, Sahar, Elhout, Somaya, Hajjaj, Ahmed N K, Hasanain, Doaa, Nabil hajjaj, Baraa, Obaid, Mohammed, Saikaly, Eman, Salhi, Ahmed, Al-Tammam, Hiba, Almasri, Murad, Baniowda, Muath, Beshtawi, Doha, Horoub, Ali, Misk, Rami, Mohammad, Bayan, Qasrawi, Rami, Sholi, Tasnim, Abu-Nimeh, Samar, Abu-srour, Abrar, Abukhalaf, Sadi A., Adawi, Samer, Alsalameh, Barah, Ayesh, Kholoud, Elqadi, Muawiyah, Hammouri, Ahmad, Karim Mustafa, Fatima, Marzouqa, Natalie, Melhem, Shatha, Miqdad, Dima, Mohamad, Balqees, Rawhi, Mhammed, Abu Ahammala, Ayman B., Abu Ataya, Ahmed, Abu Jayyab, Israa, Al-Shwaikh, Samar, Alagha, Othman, Alasttal, Mohammed, Awadallah, Haneen, Elblbessy, Mahmood, Fares, Jehad, Jarbou, Akram, Mahfouz, Ibtisam, Albahnasawi, Moath A., Abo mahadi, Asmaa', Abuelhatal, Hasan, Abuelqomboz, Ayham, Almoqayyad, Abdelrahman, Alwali, Abdallah, Balaawi, Reem, Hamouda, Mahmoud, Humeid, Mohammed, Jedyan, Abdullah, Mahmoud Abu hamam, Tasneem, Matar, Ghadeer, Salem, Ali, Samra, Tahani, Shaheen, Nureddin, Shihada, Karam, A.Nemer, Ayoob, Abu Al Amrain, Mahmoud, Abu Alamrain, Abdulwhhab, Abu Jamie, Najlaa, Abu-Rous, Mohammed R., Alfarra, Nada, AlTaweel, Mohammed, Alwhaidi, Noor, Hamed, Ramadan, Saqqa, Bader, Shaheen, Ahmad, Aljaber, Dana, Aljaberi, Loay, Alwaheidi, Malak, Jawaada, Assef, Khaldi, Hani, Qahoush, Rami, Qari, Jalil, Saadeh, Rana, Salim, Ahlam, Yacoub, Aseel, Abbas, Abbas, Abu shua'ib, Rana, Abu Zainah, Baraa, AbuSirrees, Mahmoud, Babaa, Basheer, Barhoush, Ola, Belal qadomi, Asef, Daraghmeh, Laith, Haji, Reema, Khatatbeh, Alaa, Khatib, Lana, Qarariah, Salsabeel, Quzmar, Yara, Safadi, Khalil, Salameh, Roqaya, Hassan, Mohammad, Herzallah, Shifaa, Massad, Loai, Nazzal, Ahmed, Nazzal, Ranin, Escobar, Dennis, Machain V, Gustavo Miguel, Rodriguez Gonzalez, Agustin, Chachaima Mar, Jorge Emerson, Chinchihualpa Paredes, Nathaly Olga, Cuba, Vicente, Lopez, Walter, Niquen Jimenez, Maria Milagros, Sanchez Bartra, Nestor Alberto, Sapallanay Ojeda, Olenka, Sequeiros, Diego, Toscano Pacheco, Andrea, Vergara, María, Abarca, Sol, Alcorta, Rodrigo, Borda-Luque, Giuliano, Eusebio Zegarra, Ivan Edward, Luján López, Claudia, Marrufo, Mirella, Mogrovejo, Cinthya, Nomura, Andrea, Rodríguez Angeles, Yamile, Vidal Meza, Maitza Rosario, Zavala, Gabriela, Castillo Arrascue, José Neiser, Hidrogo Cabrera, Jomara Caroline, Larrea vera, José Julio Mariano, Osorio, Miguel, Ylatoma Díaz, Edgar Alcides, Fontanilla, Mark Anthony, Fuentes, Joseph Roy, Salazar, Anna Leah, Dominguez, Genieve, Lopez, Marc Paul, Macalindong, Shiela, Onglao, Mark Augustine, Ramirez, Arjel, Sacdalan, Marie Dione, Tampo, Mayou Martin, Uy, Gemma Leonora, Mangahas, Jeremiah, Yabut, Kenneth, Cañete, Joannes Paul, Cansana, Bernalynn Eris, Castro, Ernes John, Lipana, Maria Kaiserin, Roxas, Manuel Francisco, Zara, Vlu Jean, Chroł, Maciej, Franczak, Paula, Orłowski, Michał, Budzyński, Piotr, Budzyński, Andrzej, Bury, Pawel, Czerwińska, Agata, Dworak, Jadwiga, Dziedzic, Jacek, Kisielewski, Michał, Kulawik, Jan, Lasek, Anna, Małczak, Piotr, Migaczewski, Marcin, Pędziwiatr, Michał, Pisarska, Magdalena, Radkowiak, Dorota, Rubinkiewicz, Mateusz, Rzepa, Anna, Skoczylas, Tomasz, Stanek, Maciej, Truszkiewicz, Katarzyna, Wierdak, Mateusz, Winiarski, Marek, Zarzycki, Piotr, Zub-Pokrowiecka, Anna, Kowalewski, Piotr, Roszkowski, Rafał, Walędziak, Maciej, Tomé, Miguel, Patrocinio, Sara, Guerreiro, Ines, Almeida, Filipe, de Sousa, Xavier, Monteiro, Nuno, Costa Santos, Maria Teresa, de Oliveira, Daniela, Lopes Serra, Marta, Morgado, Daniela, Neves, Christian, Oliveira, Ana Carolina, Pimentel, Alice, Silva, Sofia, Carvalho, Márcia, Carvalho, Lúcia, Magalhães, Joana, Matos, Leonor, Monteiro, Tânia, Ramos, Carlota, Santos, Vanessa, Barbosa, José, Costa-Maia, Jose, Devezas, Vítor, Fareleira, Ana, Fernandes, Cristina, Gonçalves, Diana, Mora, Henrique, Morais, Marina, Silva de Sousa, Fabiana, Catarino Santos, Sara, Logrado, Ana, Tojal, André, Amorim, Edgar, Cunha, Miguel F., Fazenda, Ana, Melo Neves, João Pedro, Sampaio da Nóvoa Gomes Miguel, Inês Isabel, Veiga, Diogo, Azevedo, José, Cardoso Louro, Hugo, Leite, Mariana, Bairos Menezes, Maria, Gama, Bárbara, Brito, Diana, Cruz Martins, Marta Cristina, Graça e Magalhães, André, Longras, Ana Catarina, Lourenço, Rita, Matos, Diana, Castro, Luis, Policarpo, Filipa, Romano, Joana, Monteiro, Cristina, Pinto, Diogo, Duarte, Marina, Fortuna Martins, Sónia, Oliveira, Mariline, Galvão, Diogo, Martins, Lisandra, Silva, Anaisa, Taranu, Viorel, Vieira, Bárbara, Neves, Jessica, Oliveira, Simone, Ribeiro, Hugo, Cinza, Margarida, Felix, Rosa, Machado, Arnaldo, Oliveira, Joana, Patrício, Joana, Pedroso de Lima, Rita, Pereira, Mário, Rocha Melo, Miguel, Velez, Cristina, Abreu da Silva, Alberto, Claro, Mariana, Costa Santos, Daniel, Ferreira, Andreia, Capote, Hugo, Rosado, Daniela, Taré, Filipa, Nogueira, Oriana, Ângelo, Miguel, Baiao, José Miguel, Guimarães, Andreia, Marques, João, Nico Albano, Miguel, Silva, Marta, Valente da Costa, Ana, Vieira Caroço, Teresa, Almeida Braga, Sara, Capunge, Ines, Fragoso, Marta, Guimarães, João, Pinto, Bruno, Ribeiro, João, Angel, Miguel, Fialho, Guilherme, Guerrero, Monica, Campos Costa, Filipa, Cardoso, Diogo, Cardoso, Vasco, Alves, Magda, Estalagem, Inês, Louro, Tiago, Marques, Cláudia, Martelo, Rita, Morgado, Miguel, Canotilho, Rita, Correia, Ana Margarida, Martins, Pedro, Peyroteo, Mariana, Gomes, João, Monteiro, Rita, Romano, Manuela, Alves, Daniela Macedo, Peixoto, Rita, Quintela, Catarina, Jervis, Maria João, Melo, Débora, Pacheco, André, Paixão, Valter, Pedro, Vera, Pimenta, Joana, Pimenta de Castro, João, Rocha, Ana, Beuran, Mircea, Ciubotaru, Cezar, Diaconescu, Bogdan, Hostiuc, Sorin, Negoi, Ionut, Stoica, Bogdan, Anokhin, Evgeny, Kuznetsov, Georgy, Oganezov, Giorgi, Paramzin, Fedor, Romanova, Ekaterina, Rutkovskii, Valeryan, Rutkovskii, Vasilii, Shushval, Mikhail, Zabiyaka, Mikhail, Dzhumabaev, Khasan, Ivanov, Valerii, Mamedli, Zaman, Achkasov, Sergey, Balkarov, Artem, Nabiev, Elnur, Nagudov, Marat, Rybakov, Evgeny, Saifutdinova, Karina, Sushkov, Oleg, Joseph, Lule, Ndayishimiye, Isaac, Faustin, Ntirenganya, Mutabazi, Alphonse Zeta, Mvukiyehe, Jean Paul, Nsengimana, Vizir J.P, Uwakunda, Carine, Abbas, Mohammad Monir, Akeel, Nouf, Aljiffry, Murad, Awaji, Kholoud, Farsi, Ali, Jamjoum, Ghader, Khoja, Ahmad, Maghrabi, Ashraf, Malibary, Nadim, Nassif, Mohammed, Saleem, Abdulaziz, Sultan, Abdullah, Tashkandi, Wail, Tashkandi, Hanaa, Trabulsi, Nora, Ba, Mouhamadou Bachir, Diallo, Adja Coumba, Ndong, Abdourahmane, Cuk, Vladica, Janković, Uroš, Koh, Sharon Zhiling, Koh, Frederick, Lee, Kuok Chung, Lee, Kai Yin, Lee, Sean, Leong, Wei Qi, Lui, Su Ann, Prakash, Prajwala, Grosek, Jan, Norcic, Gregor, Tomazic, Ales, Fitchat, Nicolas, Jaich, Robert, Wineberg, Devorah, Koto, Modise Zacharia, Baiocchi, Daniella, Clarke, Damian, Steenkamp, Christina Johanna, Bannister, Sharon, Boutall, Adam, Chinnery, Galya, Coccia, Anna, Dell, Angela, Karjiker, Parveen, Kloppers, Christo, Loxton, Nicholas, Mabogoane, Tumi, Malherbe, Francois, Panieri, Eugenio, Rayamajhi, Shreya, van Wyngaard, Tirsa, Warden, Claire, Madiba, T E, Pillay, Nivashen, Brooks, Savannah, Kruger, Charlise, Van Der Merwe, Lisa Hannah, Gool, Ferhana, Kariem, Maahir, Bougard, Heather, Kariem, Nazmie, Noor, Fazlin, Pillay, Reantha, Steynfaardt, Leandi, González González, Lucía, Marín Santos, José Miguel, Martín-Borregón, Paula, Martínez Caballero, Javier, Nevado García, Cristina, Rodriguez Fraga, Pastora, De Castro Parga, Gonzalo, Fernández Veiga, Maria Pilar, Garrido López, Lucía, Infante Pino, Hugo, Lages Cal, Irene, López Otero, Marta, Nogueira Sixto, Manuel, Paniagua García Señorans, Marta, Rodríguez Fernández, Laura, Ruano Poblador, Alejandro, Rufo Crespo, Erika, Sanchez-Santos, Raquel, Vigorita, Vincenzo, Alonso Batanero, Ester, Asnel, Dorisme, Cifrian Canales, Isabel, Contreras Saiz, Elisa, De Santiago Alvarez, Irene, Díaz Vico, Tamara, Fernandez Arias, Sebastian, Fernández Martínez, Daniel, García Bernardo, Carmen, García Flórez, Luis Joaquín, Garcia Gutierrez, Carmen, García Munar, Manuel, Márquez Zorrilla Molina, Carlos Alberto, Merayo, Marta, Michi Campos, José Luis, Moreno Gijon, Maria, Otero-Diez, Jorge L., Rodicio Miravalles, Jose Luis, Solar-Garcia, Lorena, Suárez Sánchez, Aida, Truan, Nuria, Alejandre Villalobos, Cristina, Caballero Díaz, Yurena, Jimenez, Marta, Montesdeoca, Dacil, Navarro-Sánchez, Antonio, Vega, Victor, Beltrán de Heredia, Juan, Gómez, Zahira, Jezieniecki, Carlos, Legido Morán, Ana Patricia, Montes-Manrique, Mario, Rodriguez-Lopez, Mario, Ruiz Soriano, María, Trujillo Díaz, Jeancarlos, Vazquez Fernandez, Andrea, Argudo, Nuria, Pera, Miguel, Torrent Jansà, Laia, García Domínguez, Melody, Goded, Ignacio, Roldón Golet, Marta, Talal El-Abur, Issa, Utrilla Fornals, Alejandra, Zambrana Campos, Vanesa, Aguilar Martinez, Maria Del Mar, Bosch, Marina, García-Catalá, Luis, Sánchez-Guillén, Luis, Artigau, Eva, Gomez Romeu, Nuria, Julià Bergkvist, David, Espina Perez, Beatriz, Morató, Olga, Olona, Carles, Diéguez, Beatriz, Forero-Torres, Alexander, Losada, Manuel, Gomez-Abril, Segundo, Gonzálvez, Paula, Martinez, Rosario, Navarro Martínez, Sergio, Payá-Llorente, Carmen, Pérez Rubio, Álvaro, Santarrufina Martinez, Sandra, Sebastián Tomás, Juan Carlos, Trullenque Juan, Ramon, Gegúndez Simón, Alberto, Maté, Paloma, Prieto-Nieto, Maria Isabel, Rubio-Perez, Ines, Urbieta, Aitor, Vicario Bravo, Marina, Abelló, David, Frasson, Matteo, Garcia-Granero, Alvaro, Abad Gurumeta, Alfredo, Abad-Motos, Ane, Lucena-de Pablo, Elena, Nozal, Beatriz, Ripollés-Melchor, Javier, Salvachúa, Rut, Ferrero, Esther, Garcia-Sancho Tellez, Luis, Picardo, Antonio L., Rojo López, Jose Alberto, Zorrilla Matilla, Laura Patricia, Cagigas Fernandez, Carmen, Castanedo Bezanilla, Sonia, Estevez Tesouro, José, Fernandez-Diaz, Maria Jose, García Cardo, Juan, Gomez Ruiz, Marcos, Gonzalez-Tolaretxipi, Erik, Jimeno Fraile, Jaime, Poch, Cristobal, Rodriguez-Aguirre, Montserrat, Troche Pesqueira, Noemí, Trugeda-Carrera, Maria Soledad, de la Torre, Javier, Blanco-Colino, Ruth, Espin-Basany, Eloy, Espinosa-Bravo, Martin, Morales Comas, Clara, Reyes Afonso, Eduardo, Rivero Déniz, Joaquín, Siso Raber, Christian, Verdaguer Tremolosa, Mireia, Chandrasinghe, Pramodh, Kumarage, Sumudu, Wijekoon Arachchilage, Nimeshi, Abdalla Ahmed Elkamel, Ahmed, A. Adam, Mohammed, Blomme, Nina, Thorell, Anders, Wogensen, Fredrik, Älgå, Andreas, Ansarei, Dhirar, Celebioglu, Fuat, Heinius, Göran, Nigard, Linda, Pieniowski, Emil, Ahlqvist, Sandra, Björklund, Ida, Frånberg, Andreas, Håkansson, Martina, Adamo, Karin, Franklin, Oskar, Sund, Malin, Wiberg, Rebecca, Andersson, Yvette, Chabok, Abbas, Nikberg, Maziar, Kugelberg, Alexander, Canonica, Claudia, Christoforidis, Dimitrios, Fasolini, Fabrizio, Gaffuri, Paolo, Giuliani, Mauro, Meani, Francesco, Popeskou, Sotirios Georgios, Pozza, Silvia, Wandschneider, Wiebke, Peterer, Lorenz, Widmer, Lukas Werner, Zimmermann, Bernd, Bakoleas, Panagiotis, Chanousi, Iris, Charalampidou, Lydia, Grochola, Lukasz Filip, Heid, Franziska, Ntaoulas, Sotirios, Outos, Michail, Peros, Georgios, Podolska-Skoczek, Hanna, Reinisch, Katharina Beate, Zielasek, Christian, Demartines, Nicolas, Gilgien, Jérôme, Kefleyesus, Amaniel, St-Amour, Pénélope, Toussaint, Arnaud, Alhimyar, Maryam, Alsaid, Bayan, Alyafi, Amr, Alkhaledi, Ahmad, Kouz, Basel, Omarain, Ahmad, Al-Sabbagh, Yusra, Alkhatib, Haya, Sara, Samer, Alhaj, Ahmad, Danial, Aghyad, Kadoura, Lama, Maa Albared, Sarah, Monawar, Yamen, Nahas, Louei, Abd, Barook, Saad, Ahmad, Wakkaf, Habib, Bouzaiene, Hatem, Ghalleb, Montassar, Akaydin, Elif, Akbaba, Ata Cem, Atakul, Onur, Baltaci, Ege, Besli, Sevval, Burgu, Gökçen, Cenal, Ulukan, de Muijnck, Cansu, Demirkaya, Hasan Can, Dogruoz, Alper, Gezer, Zeynep Ipek, Gündoğdu, Yasemin, Kara, Merve, Korkmaz, Hasan Kürşad, Kurtoğlu, Gökalp Kağan, Ozben, Volkan, Ozmen, Berk Baris, Pektaş, Ahmet Murat, Sel, Eda Kübra, Yenidünya, Nilüfer, Bengur, Fuat Baris, Oral, Berke Mustafa, Yozgatli, Tahir Koray, Abdullayev, Seymur, Gunes, Mehmet Emin, Sahbaz, Nuri Alper, Banaz, Tuba, Kargici, Kübra, Kuyumcu, Omer Faruk, Yanikoğlu, Erkan, Yeşilsancak, Merve, Yilmaz, Duygu, Aktas, Melik Kagan, Rencuzogullari, Ahmet, Isik, Arda, Leventoğlu, Sezai, Yalçinkaya, Ali, Yüksel, Osman, Kalayci, Mustafa U, Kara, Yasin, Sarici, Inanc Samil, Akin, Alp, Alemdağ, Gökçe nur, Arslan, Ekin, Baki, Bahadir Emre, Bodur, Muhammed Selim, Calik, Adnan, Candas Altinbas, Bahar, Cihanyurdu, Irem, Erkul, Oğuz, Gül, Burak, Guner, Ali, Köse, Beyza, Semiz, Anil, Sevim, Şule, Tayar, Serkan, Tomas, Kadir, Tüfek, Ozan yavuz, Türkyilmaz, Serdar, Uluşahin, Mehmet, Usta, Arif, Yildirim, Reyyan, Güler, Sertaç Ata, Tatar, Ozan Can, Varol, Ecenur, Kirimtay, Busenur, Uysal, Muhammed, Yildiz, Alp, Kose, Emin, Ciftci, Ahmet Burak, Çolak, Elif, Eraslan, Huseyin, Kucuk, Gultekin Ozan, Yemez, Kürşat, Lule, Herman, Bienfait, Mumbere, Bua, Emmanuel, Okalany, Noella, Basarab, Maksym, Bielosludtsev, Oleksii, Kolhanova, Kateryna, Perepelytsia, Kateryna, Romanukha, Kateryna, Savenkov, Dmytro, Siryi, Stanislav, Tereshchenko, Maksym, Viacheslav, Nezamai, Volovetskyi, Anton, Kebkalo, Andrey, Tryliskyy, Yegor, Tyselskiy, Volodimir, Bruce, Eilidh, Chow, Bing Lun, Iddles, Emma, McGuckin, Sarah, Newall, Nicola, Ramsay, George, Sharma, Parivrudh, Stewart, Caitlin, 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Sule, Davies, Justine, Donaldson, Rachel, Ede, Chikwendu, Garden, O James, Lesetedi, Chiapo, Mabedi, Charles, Magill, Laura, Makinde Alakaloko, Felix, Makupe, Alex, Monahan, Mark, Mulira, Soloman, Muller, Elmi, Musowoyo, Jospeh, Olory-Togbe, Jean Léon, Roberts, Tracey, Smith, Martin, Tayler, Viki, Windsor, John, Yepez, Raul, Sundar, Sudha, Runigamugabo, Emmy, Verjee, Azmina, Chen, José, Daya, Leonid, El Aroussi, Nouhaila, Farina, Valeria, Gnintedeme Olivier, Tchianze, Gonzales Nacarino, Mauricio, Hammani, Aamr, Honjo, Sarah, Jacobs, Rebecca, Kimura, Hitomi, Nkoronko, Mugisha, Oscullo Yepez, Jasson Javier, Pin Hung, Wei, Raj, Ankit, Romani Pozo, Alina, Rommaneh, Muna, Sassamela Fabiano, Samuel Chimbioputo, Shiroma Gago, Camila Milagros, Srinivas, Abhishekh, Sung, Chia-Yen, Tai, Aswan, Valle Aranda, Yener Cristyell, Venturini, Sara, and Wilguens Lartigue, Jean

Published
2022
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50. Towards seamless multi-view scene analysis from satellite to street-level

Author

Lefèvre, Sébastien, Tuia, Devis, Wegner, Jan Dirk, Produit, Timothée, and Nassar, Ahmed Samy

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we discuss and review how combined multi-view imagery from satellite to street-level can benefit scene analysis. Numerous works exist that merge information from remote sensing and images acquired from the ground for tasks like land cover mapping, object detection, or scene understanding. What makes the combination of overhead and street-level images challenging, is the strongly varying viewpoint, different scale, illumination, sensor modality and time of acquisition. Direct (dense) matching of images on a per-pixel basis is thus often impossible, and one has to resort to alternative strategies that will be discussed in this paper. We review recent works that attempt to combine images taken from the ground and overhead views for purposes like scene registration, reconstruction, or classification. Three methods that represent the wide range of potential methods and applications (change detection, image orientation, and tree cataloging) are described in detail. We show that cross-fertilization between remote sensing, computer vision and machine learning is very valuable to make the best of geographic data available from Earth Observation sensors and ground imagery. Despite its challenges, we believe that integrating these complementary data sources will lead to major breakthroughs in Big GeoData.

Published
2017
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