dataesr/scientific-paragraphs-categorization huggingface dataset:
The massive increase in the number of sequenced genomes makes it very challenging to share and analyze these datasets. There is a very strong need for new methods and frameworks that will enable decreasing the cost and facilitate analysis and management of genome sequencing. One of the main techniques used for decreasing the cost of large-scale genotyping is in-silico genotype imputation, i. e. , measuring genotypes at a subsample of variants, e. g. , using a genotyping array and then utilizing the correlations among the genotypes of nearby variants (the variants that are close to each other in genomic coordinates) and imputing the missing genotypes using the sparsely genotyped variants  [15] [16] [17]. Imputation methods aim at capturing the linkage disequilibrium patterns on the genome. These patterns emerge because the genomic recombination occurs at hotspots rather than at uniformly random positions along the genome.
Although imputation methods can predict only the variant genotypes that exist in the panels, the panels' sample sizes are increasing rapidly, e. g. , in projects such as TOPMed  24, 25  will provide training data for imputation methods to predict rarer variant genotypes, and this can increase the sensitivity of GWAS. Although imputation and sparse genotyping methods enable a vast decrease in genotyping costs, they are computationally very intensive and require management of large genotype panels and interpretation of the results  26. The imputation tasks can be outsourced to third parties, such as the Michigan Imputation Server, where users upload the genotypes (as a VCF file) to a server that performs imputation internally using a large computing system. The imputed genotypes are then sent back to the user.
We hope that our study can provide a reference for the development of privacy-aware and fully secure approaches that employ homomorphic encryption. Second, the amount of genomic data has increased several orders of magnitude in recent years. This provides enormous genotype databases where we can train the imputation models and test them in detail, before implementing them in secure evaluation frameworks. Another significant development is the recent formation of genomic privacy communities and alliances, i. e. Global Alliance for Genomic Health (GA4GH), where researchers build interdisciplinary approaches for developing privacy-aware methods. For example, our international study stemmed from the 2019 iDASH Genomic Privacy Challenge. We firmly believe that these communities will help bring together further interdisciplinary collaborations for the development of secure genomic analysis methods.
Variant and Genotype Datasets All the tag and target variant loci, and the genotypes are collected from the public resources. We downloaded the Illumina Duo 1M version 3 variant loci from the array's specification at the Illumina web site (https://support. illumina. com/downloads/human1m-duo_v3-0_product_files. html). The file was parsed to extract the variants on chromosome 22, which yielded 17,777 variants. We did not use the CNVs and indels while filtering the variants and we focused only on the single nucleotide polymorphims (SNPs). We then intersected these variants with the 1000 Genomes variants on chromosome 22 to identify the array variants that are detected by the 1000 Genomes Project. We identified 16,184 variants from this intersection. This variant set represents the tag variants that are used to perform the imputation.
Micro-AUC treats the imputation problem as a three-level classification problem where each variant is "classified" into one of three classes, i. e. , genotypes, {0, 1, 2}. Micro-AUC computes an AUC metric for each genotype then microaggregates the AUCs for all the genotypes. This enables assigning one score to a multi-class classification problem. We use the implementation in scikit-learn package to measure the micro-AUC scores for each method (https: //scikit-learn. org/stable/modules/generated/sklearn. metrics. roc_auc_score. html). Measurement of Time and Memory Requirements. For consistently measuring the time and memory usage among all the benchmarked methods, we used "/usr/bin/time -f %e "\t" %M" to report the wall time (in seconds) and peak memory usage (in kilobytes) of each method.
More precisely, each tag variant is first encoded as a polynomial with its coefficients, and the encoded plaintext is encrypted into a ciphertext using the underlying HE scheme. The plaintext polynomial in the BFV scheme is separated from an error polynomial (inserted for security), whereas the plaintext polynomial in the CKKS scheme embraces the error. Then Eq. (  2 ) is homomorphically evaluated on the encrypted genotype data by using the plain weight parameters. We exploit parallel computation on multiple individual data, and hence it enables us to obtain the predicted genotype estimates over different samples at a time. Our experimental results indicate that the linear model with 32 tag variants as features for each target variant shows the most balanced performance in terms of timing and imputation accuracy in the current testing dataset (see Supplementary Table  8  and Supplementary Figure  4 ).
Of course, as neither bootstrapping nor key-switching is used in our solution, the key distribution can be changed to any distribution (including the full domain distribution) without any time penalty. Our scheme achieves 130 bits of security, according to the LWE estimator  55. More information about the our solution is described in the supplementary document ("Chimera-TFHE team solution"). EPFL-CKKS. EPFL uses a multinomial logistic regression model with d -1 neighboring coefficients and 1 intercept variable for each target variant, with three classes {0,1,2}. The plaintext model is trained using the scikit-learn python library. The input variants are represented as values {0,1,2}. There is no pre-processing applied to the training data. For a target position j, the predicted probabilities for each class label are given by: P[y = g|z (p, j) r ] = e w (•, j,g) 0 +∑ d-1 r=1 w (•, j,g) r ∑ 2 i=0 e w (•, j,i) 0 +∑ d-1 r=1 w (•, j,i) r z (p, j,•) r , (3) where {w d-1 } are sent encrypted and packed to the server, using the CKKS homomorphic cryptosystem, and the exponents in Eq.
Minimac3 Minimac3 is downloaded from the University of Michigan web site. We next downloaded Eagle 2. 4. 1 phasing software. 'Eagle+Minimac3' is used in the Michigan Imputation Server's pipeline that is served for the public use. The panels are converted into indexed VCF files as required by Eagle and Minimac3. We first used the Eagle protocol to phase the input genotypes. The phased genotypes are supplied to the Minimac3, and final imputations are performed. Both eagle and Minimac3 were run with 16 threads using the command line options ('-numThreads=16' and '-cpus 16' options for Eagle and Minimac3, respectively). Minimac3 reports an estimated dosage of the alternate allele, which we converted to a score as in the above equation for UTMSR's scoring. then sent to the researcher (Step 6). The researcher decrypts the genotype probabilities (Step 7) and predicts the genotypes with highest probabilities and builds the final set of complete genotypes (Step 8).
Nowadays, there is a huge amount of Historical Arabic Documents (HAD) in the national libraries and archives around the world. Analyzing this type of data manually is a difficult and costly task. Thus, an automatic process is required to exploit these documents more rapidly. Processing historical documents is a recent research subject that has seen a remarkable growth in the last years. Processing Historical Arabic Documents is a particularly challenging problem. First, due to complicated nature of Arabic script compared to other scripts and second because the documents are ancient. This paper focuses on this difficult problem and provides a comprehensive survey of existing research work. First, we describe in detail the challenges making the automatic processing of Historical Arabic Documents a difficult task.
Introduction Documents are frequently utilized in everyday life. The need to analyze them and understand their content is permanent. In the past, processing the documents was achieved manually due to the lack of large and high quality digital datasets where an automatic process can be learned. In fact, due to the scarcity of iq high quality digital scanning solutions and iiq high storage capacity devices, converting and storing document images from paper format to digital format was a challenging task. Recently, this task has become easier thanks to impressive advances in digital scanning and electronic storage solutions  [87]. Consequently, the amount of numeric document datasets has expanded dramatically. This calls for automatic documents processing to understand their content and meet the users various queries such as iq searching some keywords in the document iiq identifying the writer of an unknown document, iiiq indexing a document to facilitate its use, etc.
When the writer is unknown, we can try to find some information about him. This can be done first by comparing the document to other writer-known documents in the library (Writer Identification (WI)). Second, one can retrieve, out of the database, the documents images written by the same writereven unknown (Writer Retrieval (WR)). We call these two procedures, Writer Classification. • Document Classification: In the context of historical documents, it is often difficult to recognize each character separately. It is however possible to conclude some information about the document by considering a higher level of granularity: a sub-word, a word, or even the whole document, as described below. -Word Recognition (WR): The idea is to consider some words or sub-words of the document and recognize them through a matching with known words in the reference dataset to understand the content of the document.
Contributions This paper focuses on the Automatic Processing of Arabic Historical Documents. In the past, researchers were interested on processing iq Arabic Non-Historical documents  [76, 67, 48, 75, 7]  or iiq Non-Arabic Historical Documents  [44, 49, 64]. As discussed in the introduction, processing Historical Arabic Documents is a more challenging task. Thanks to the increasing number of HAD digital datasets, this research subject is gaining more attention. Analyzing existing solutions can help understanding the problem and identifying other issues. However, to the best of our knowledge, there is no survey summarizing the Automatic Processing of Arabic Historical Documents literature. This paper fills this gap and provides two essential contributions: • Although some surveys of problems similar to the APHAD exist, they can not provide a comprehensive view of the APHAD problem.
In fact, the solutions proposed for other document types (non degraded, non Arabic) are not necessarily adapted to Arabic Historic Documents. For example, Optical Character Recognition (OCR)  [90]  used for non degraded documents is not suitable to Historical Documents. This paper summarizes existing solutions only proposed and validated on Historical Arabic Datasets. • For each HAD application, existing approaches are discussed and qualitatively compared, then some recommendations are provided. We think that this survey can be a first guide to researchers interested on APHAD.
Paper organization The paper is structured as follows. Section 2 describes the challenges involved on the APHAD. Sections 3, 4, 5 and 6, present the state of the art of the four APHAD applications, respectively, Document Analysis, Writer Classification, Document Classification and Data Retrieval. For each task, the existing approaches are described in detail. Section 7, focuses on datasets and softwares used on the APHAD problem. Finally, conclusions and perspectives are drawn in section 8.
Text Analysis Text Line Detection Top-down  [17]  Private (217 pages)  [83]  Private (315 pages) Hybrid  [31]  Private (836 pages)  Apply a Energy Map algorithm  [63] , then compute seems using dynamic programming. Seams propagate according to energy maps Gray-scale document image Dataset of 217 page (2520 lines). It includes Arabic, English and Spanish Historical Documents 96. 05%  [83]  Apply an Energy Map algorithm  [18]  then compute seems The segmentation of a document image into a set of text lines can be an important step for several HAD applications such as text alignment, word spotting, etc. Although text line detection has received a lot of attention, it still faces many challenges related to the: iq document image quality and iiq the writing style. In fact, a historical document is usually of low quality due to its age, its storage conditions and the frequent manipulations.
The authors selected the Energy Map algorithm proposed in  [63] , then seams are computed using dynamic programming. The medial and separating seams propagate according to the energy maps, which are defined based on the constructed distance transform. The approach is evaluated on a dataset of 217 pages (2520 lines). It includes Arabic, English and Spanish Historical Documents 1. The Arabic part includes 97 pages (900 lines) from "Juma Al-Majid Center for Culture and Heritage"  [Abe]. In  [83] , the authors of  [17]  evaluated their approach on more different datasets. They proposed an equivalent approach that takes as input a binary document image, instead of gray-scale document image. The approach shares the same principle of  [17]. Its idea is to seek paths that cross the middle of text lines (the "black" regions).
Hybrid approaches In  [31] , the authors proposed a Hybrid approach to segment the text into lines. It consists of segmenting a document image into components, then components are fused to form lines. This approach includes three major steps: iq Line enhancement for slight skew, iiq Image binarization, and iiiq Energybased minimization. Given a gray-scale document image as input, first, a Gaussian based multi-scale filter is applied to enhance text lines in the image. Then, a binarization algorithm is applied, based on component-tree  [72]. Finally, an energy minimization step is applied to assign connected components to text lines. In evaluation, the authors used public datasets written in different languages having variable image qualities, such as ICDAR2013  [96]  and ICDAR2009  [43]. Moreover, the authors constructed their own dataset 2 composed of 25 historical manuscripts with 261 text lines written in Arabic from the Islamic Heritage Project (IHP 3 ).
Discussion As shown in the literature, a line detection system takes as input a gray-scale or a binary image. As mentioned in  [87] , the use of gray-scale format is preferred since applying image binarization on low quality images can introduce noise and various artifacts. Note that the performance of a line detection system depends on the nature of the input images. Most datasets used for evaluation contain non-degraded documents. We think that conceiving line detection systems for degraded document requires more investigation. In some cases, to better detect lines, some preprocessing steps should be applied to detect and correct the skew angle of the text as proposed in  [35]. Finally, it is worthy to mention the work of  [79]  even if it is out of the scope of this review since it is not validated on a HAD datasets.
Model-based approaches In this category of approaches, the layout segmentation task is formulated as a classification problem. A prior model is learned from a training dataset to model the characteristics of note-side and maintext. In  [29] , the characteristics are learned via the AutoMLP  [27]  classifier. While in  [23] , a Fully Convolutional Network classifier is used. In  [29] , each component from the document image is classified as main-text or side-notes. The approach consists of three major steps: iq Features extraction, iiq Training and iiiq Labeling refinement. First, each component is described by two types of features related to its shape and context: 1q Component shape features are related to its height, foreground area, orientation, etc. and 2q Component context features are related to its neighbors components.
Second, an AutoMLP  [27]  classifier is trained using a manually labeled dataset. The classifier takes as input 13 thousand main-text components and 12 thousand side-notes components. Finally, after classifying all the components of the input document image, each component label is refined (updated) using its neighbor labels, keeping a spatial coherence. In  [23] , the two steps, features extraction and training are fused in a Fully Convolutional Network (FCN) classifier. It takes as input patches of the two classes (main-text and side-notes) and outputs the predicted class. The FCN classifier is learned from 100. 000 patches and validated on 20. 000 patches.
Discussion Often, the binarization step is avoided since it can produce noise and affect the performance of document processing tasks. Most researches prefer to use the original format that contains the maximum of available information. Regarding the text skew information, it can be helpful for some HAD tasks like the writer classification where scribes can be identified by their writing style. However, in others HAD tasks like line detection, text skew can be seen as a disruption and should be corrected for better segmentation. Text skew correction task is not well investigated. The only work tackling the problem is  [35] , but no information is mentioned about the size of the used dataset, thus we can not confirm the performance of the approach.
Problem description Given a query document image, it is important to know his/her writer (Writer Identification) or retrieve, out of a set of documents, the document images written by the same writer (Writer Retrieval). In both cases, we are talking about Writer Classification. In more detail, these two applications can be defined as follows : • The writer identification task assigns a query document written by an unknown writer to a known writer in a reference dataset. • The aim of the writer retrieval task is to select, or rank, the reference documents that are probability written by the same writer as the query document's based on their similarities. Writer retrieval can be used in the following scenario: having a part of a manuscript, the writer retrieval helps finding the rest of the manuscript in a huge amount of documents.
In this paper, we group these two procedures under the name of Writer Classification (WC). During the last years, WC has gained more interest, essentially due to iq the huge amount of historic manuscripts with unknown authors (eg. in the archive of national libraries), and iiq the large number of documents found with scattered pages. In the literature, a WC approach is generally performed in two steps: iq writer representation and iiq writer features matching. Often, a preprocessing step is applied to reduce noise. 1. In the writer representation step, the writer is described by a set of features extracted from the document image to reflect his/her writing style (for example, the alignment of the text, the density of words per line, etc. ). 2. During the writer features matching the query manuscript features are compared to those of a reference dataset composed of manuscripts with known writers.
Depending on the input document, we can enumerate two features matching levels, iq manuscript level and iiq page level: • At the manuscript level, each query manuscript is considered as one request which writer needs to be determined. • At the page level, each page from the query manuscript is classified independently of the others pages, then all query page labels are combined to determine the writer of the query manuscript. Depending on the solution used for features matching, WC approaches can be divided into two categories: • Model-free approaches where the features are compared using a distance metric  [12]  or the Nearest Neighbor classifier  [19, 41]  • Model-based approaches where the features are compared to a pre-trained model, learned from a training dataset. In  [40] , the pre-trained model is learned using the Support Vector Machine classifier  [98].
Model-free approaches The authors of  [41]  evaluated different features and used the Nearest-Neighbor classifier  [33]   [68]  and the Oriented Basic Image Features  [65]. As Image-Based Features, the authors assessed the Histogram of Oriented Gradients features  [34]  and Contour-Based Features  [30]. Note that to match two page features having a variable number of IPs, the authors converted, for each page, all IPs features to one feature. Moreover, a preprocessing step was applied to minimize the inherent noise and obtain a clean binarized version of the manuscript pages. A similar work was proposed in  [19]. It used the Contour-Based Features and the Gradient-Based Features to describe a document image, and the Nearest Neighbor classifier for matching. The major difference compared to  [41]  is the dataset used for the evaluation (cf.
Model-based approaches In the writer identification task, it is hard to assume that a query manuscript associated with an unknown writer is necessarily written by an author existing in the reference database. Finding one reference manuscript which reveals the highest similarity with the query document does not systematically imply that the two manuscripts were written by the same scribe. In this context, in  [40] , the same authors of  [41]  proposed a two-stages approach to reject non correct identification. In the first stage, for each query page, the best ranked pages and their corresponding writers in the reference dataset are retrieved. The authors used the same features and classification method, proposed in  [41] , to select the most similar pages from the reference dataset. The result is a ranked list of retrieved pages, sorted by the internal confidence of the retrieval system in the correctness of the respective page.
Discussion As shown in the literature, the writer classification task was often tackled while assuming two hypotheses: 1. All the pages of the query manuscript were written by the same scribe. 2. The reference dataset include systematically the actual writer of the query manuscript. Some studies have ignored one or the other of these hypotheses (  [12]  ignored 1q and  [40]  ignored 2q). However, given that these two hypotheses have the same importance, an approach resolving the writer classification issue while respecting both of them is required. During the writer classification step, more parameters that can temporarily influence and change the scribe writing style like the writer anatomic conditions, age, etc. , should be investigated and taken into account. Such parameters were considered in  [2]  where the authors focused on classifying writing styles in the same document.
The authors proposed to use Siamese Nets  [28]  based on Siamese convolutional Neural Network to measure the small changes in the writing style of the same person. As shown in Tab. 4, a document image is mostly described by texture features that reflect the scribe handwriting style. The use of such features is well coherent with the use of handwriting datasets for their evaluation. However, in machine printed document datasets, the scribe writing style is absent, thus the distinction between the scribes should be rather based on the documents content. Given an input document image, it is important to understand and recognize its content. In the context of historical documents, it is often difficult to recognize each character separately. Two text classification tasks are investigated in the state of the art according to the input type:.