dataesr/scientific-paragraphs-categorization huggingface dataset:
Related works The first version of wordnet  (Fellbaum, 1998)  was developed for English at Princeton University. It's a large lexical database where words having the same part of speech (Nouns, verbs, adjectives, adverbs) are gathered in sets of cognitive synonyms (synsets), each one expressing a distinct concept. Each word can belong to one or more synsets. The resulting synsets are connected by means of conceptual-semantic and lexical relations well labeled such as hyponymy and antonymy. The success of the Princeton WordNet has motivated the development of similar resources for other languages, such as EuroWordNet, EWN  (Vossen, 1998)  interlinking wordnets of several European languages (Dutch, Italian, Spanish, German, French, Czech and Estonian); Balkanet  (Tufis, 2004)  comprising wordnets of the Balkan languages; and recently Arabic Wordnet (AWN)  (Elkateb et al.
TD words extraction To successfully extract all TD words, the input texts must be preprocessed. In our study, the preprocessing consists, firstly, to clean the input files so as to identify the textual content. The cleaned texts are then segmented in order to extract all existing TD words. Cleaning a raw textual source is necessary in our approach because the documents are collected from the Web. All non-textual data such as images, advertisements, scripts, etc. have to be eliminated. For this purpose, we have developed a module that removes all unwanted parts from the input documents. The cleaned texts are then segmented into elementary textual units and the obtained TD words are extracted and stored in CSV files. The Table  2  gives statistics about the TD words composing the MultiTD corpus.
TD words clustering using k-modes algorithm The TunDiaWN construction is based on a semiautomatic process in which the validation tasks performed by experts are crucial. As Table  2  Shows, the MultiTD corpus includes a huge number of TD words. The manual analysis and organization of such large data looks wasteful and time consuming. In order to support experts in the organization and validation tasks and guide them during the construction process, we propose a clusteringbased method to automatically arrange the TD words set into groups. The method aims to suggest possible organizations of the given TD words by gathering them into meaningful clusters. To enhance similarities and meanings into the produced groups, we propose to cluster the TD words according to their TD roots. We rely here on the derivational morphology that characterizes the Tunisian dialect as well as the Standard Arabic.
The K-modes algorithm extends K-means  (Forgy, 1965; MacQueen, 1967)  paradigm to cluster categorical data by removing the numeric data limitation. Indeed, the K-modes algorithm introduces a new simple matching dissimilarity measure for categorical data objects. The algorithm replaces means of clusters with modes, and uses a frequency based method to update modes in the clustering process. The choice of K-modes clustering algorithm is mainly motivated because of its widely use in real world applications due to its efficiency in dealing with large categorical database  (He et al. , 2011). K-modes algorithm is also faster than other clustering algorithms (mainly k-means) since it needs less iteration to produce a stable distribution. The K-modes algorithm requires a similarity measurement to be used between the objects.
In our case, we propose to use the N-Gram similarity measurement between words. N-Gram is language independent in nature and doesn't require specific resources to be applied. Therefore, Ngram model seems suitable for dealing with a Tunisian dialect context. We applied the N-Gram distance proposed by  Kondrak (2005)  and we used the implementation provided by Apache Lucene spellchecking API 5. The K-modes algorithm consists of the following steps: a) Select K initial modes, one for each of the cluster. b) Allocate data object to the cluster whose mode is nearest to it, according to the simple matching dissimilarity c) Compute new modes of all clusters. d) Repeat step b to c until no data object has changed cluster membership. The classical K-modes algorithm assumes that the number of clusters, K, is known in advance and the clusters' modes are randomly initialized.
TunDiaWN structure As our target language is an Arabic Dialect and therefore likely to share many of the Standard Arabic concepts, we decide to preserve the AWN design. However, the AWN current structure is unable to support the specificities of the Tunisian dialect lexicon. The proposed TunDiaWN structure is then enriched by new features, entities and relations. Moreover, we aim to create a parallel resource which maintains the linkage between Tunisian dialectal, Arabic as well as English synsets and words. That's why AWN and PWN contents are preserved rather than the structures. Thus, the proposed database is designed to be able to support English, Tunisian and Standard Arabic content and correspondence. In this section, we detail the structure of the proposed TunDiaWN database and we focus on the new features we added to keep up the TD vocabulary particularities, compared to the SA and English ones.
TWN entity types The database structure incorporates mainly the following entity types: synset, word, form, synset relations, words relations, annotator: Synset: includes English and Arabic synsets. A synset has descriptive information such as Name, POS (Part Of Speech), root (Boolean feature indicating if the target synset is a root or not). Word: comprises words from different languages. In addition to the unique identifier, every word is described by his value, and a Boolean "valid" attribute which indicates if one word is already validated by experts or not yet. Form: includes mainly the root of Arabic as well as Tunisian dialect words. Synsets relations: includes links relating two synsets, like "has_instance", "equivalent", "similar", etc. We preserve here all sunsets' links without adding new ones.
Morphology Since the Tunisian dialect has no standard orthography, one word can be written in many forms using Arabic letters or Latin script. For example, the word "will" can be expressed in different ways: "bech"/ " ‫شْ‬ َ ", "bich" /" ْ ِ ", "mich"/" ْ ِ ". To deal with this situation, our database structure is enriched by a new entity named "morphology" which allows storing all versions of a given TD word.
After that, the TD experts analyze and confront the lists in order to find new synsets enrichment opportunities. The TD words qualified to be retained are those maximizing the synset harmony. The TD experts must also fill in the necessary attributes related to the added words and manually make the necessary changes and enrichments. In fact, the added words have to be described according to the new features added to the Tun-DiaWN database, so as to bring different knowledge of different vocabularies and give all useful details related to the target word.
Conclusion and future works We have described an approach for building a Tunisian dialect lexical resource which takes advantages of online TD resources and reuses Wordnets of other languages. The proposed TunDiaWN can be considered as parallel TD-SA resource since it preserves the AWN content. Thanks to the novel added TD attributes, the TunDiaWN design provides, also, great opportunities to deal with the lack of a standard written form and other specificities of the Tunisian dialect. The construction process begins with the Mul-tiTD corpus construction from many sources. After preprocessing the collected texts, the TD extracted words are gathered according to their common TD roots. Our aim at this level is to support the TD experts in the database enrichment task, by giving suggestions of the possible TD words organizations.
Tunisian dialect textual data collect: it consists in producing our MultiTD corpus (Multi-source Tunisian dialect corpus) which gathers TD texts from many sources: social networks (Twitter, Facebook, etc. ), written pieces of theater, dictionaries, transcriptions of spontaneous speech, etc. b. TD words extraction: is to preprocess the produced corpus in order to preserve useful data and extract TD words. c. TD words clustering: we propose here a clustering based method that aims to group the extracted TD words into meaningful clusters, which represent great suggestions for possible enrichments of TunDiaWN. d. TunDiaWN enrichment: this step is performed by the TD experts. It includes the manual validation of the suggestions proposed by the previous step. We propose, in this stage, a new database structure for TunDiaWN.
a. Tunisian dialect textual data collect: it consists in producing our MultiTD corpus (Multi-source Tunisian dialect corpus) which gathers TD texts from many sources: social networks (Twitter, Facebook, etc. ), written pieces of theater, dictionaries, transcriptions of spontaneous speech, etc. b. TD words extraction: is to preprocess the produced corpus in order to preserve useful data and extract TD words. c. TD words clustering: we propose here a clustering based method that aims to group the extracted TD words into meaningful clusters, which represent great suggestions for possible enrichments of TunDiaWN. d. TunDiaWN enrichment: this step is performed by the TD experts. It includes the manual validation of the suggestions proposed by the previous step. We propose, in this stage, a new database structure for TunDiaWN.
Data was gathered and analysed from a large array of institutions, including the National Institute for Statistics (NIS), Ministry of National Education (MEN), the National Agency for Community Programs in Education and Professional Training (ANPCDEFP), the Romanian Agency for Quality Assurance in Higher Education (ARACIS), etc. The institutional self-assessment reports were developed using the same selfassessment instrument for all institutions in the sample. This instrument was based on a version initially developed by the International Association of Universities (IAU) and revised in 2012 for the purpose of the UEFISCDI project mentioned above. This ensured its fitness for purpose and its relevance within the Romanian higher education context. The study visits included semi-structured interviews conducted by the expert panel with relevant university representatives and stakeholders (rectors, senate members, administrative staff, teachers and students).
Additionally, in 1996, Romania joined the SOCRATES Programme with an important component-Erasmus. Since 1991 the Romanian Government has continuously encouraged students from the Republic of Moldova  (Basarabia)  to study in Romania by offering them targeted scholarships, as part of its larger foreign policy strategy. In terms of student mobility statistics, it is important to highlight that at the national level, Romania does not have a clear record of students who have benefited by a mobility period. There are various reports, studies and statistical series based on different definitions of mobility, but there is no centralised database with the student numbers and countries in which they experienced academic mobility. In Romania there are at least two data sets regarding mobility: one is the data set based on numbers provided by universities participating in the data collection process for the university classification and study programs ranking (published in May 2011) and the other one is the data collected by the National Institute of Statistics (NIS).
The only policy, which stands out in terms of coherence, support and cooperation among governmental actors, is that referring to ethnic Romanians. In the absence of a coherent national policy on internationalisation, the ways and means in which universities have pursued internationalisation differ widely, highly influenced by their mission and overall capacity. There is no shared understanding of this process and its importance for the future of higher education in Romania beyond a relatively small group of higher education leaders and academics. Romania does not have a comprehensive overview of country-level bilateral or multilateral cooperation partnerships; there is no comprehensive database regarding the number of mobile students and academic staff, their country of origin or other relevant data.
To improve diagnostic accuracy, all patients had at least an 18-month followup in the National Reference Center for Rare Dementias or in the Resource and Research Memory Center of the Pitié-Salpêtrière Hospital, in order to validate the diagnosis according to their clinical evolution. Clinical and demographical data are shown on Table  1 , as well as scores in facial emotions recognition test (please see  Funkiewiez et al. , 2012  for a complete description of this task). Behaviors of patients were compared to those from twelve age and education matched healthy controls. They were either spouses of unrelated patients or recruited via advertisements in the hospital. Controls were included according to the following criteria: (a) Mini Mental State Examination  (MMSE-Folstein et al. , 1975)  score > or = 27/30 and Frontal Assessment Battery  (FAB-Dubois et al.
, 2000)  > or = 16/18; (ii) no history of neurological or psychiatric disorders; (iii) no memory complaint or cognitive impairment; (iii) no history of drug abuse. This study was conducted at the National Reference Center for Rare Dementias, and in the Neurology Department (Pitié-Salpêtrière Hospital). All clinical data were generated during a routine clinical work-up and were extracted for the purpose of this study. According to French legislation, patients were informed and consented that their data might be used in retrospective clinical research studies.
Scanning electron microscopy The SEM analyses have been carried out using the scanning electron microscope Philips XL30 (20 kV), equipped with the energy-dispersive X-ray detector from Oxford Instruments. Data were acquired and processed using the INCA software. The cross-sections were embedded in resin, cut, polished and coated with carbon. The SEM-FEG images have been obtained using the scanning electron microscope Jeol JSM-7800 F operated at 5 kV. To improve image quality, crosssections were argon ion polished  [7]  and coated with platinum/iridium.
The system records a full spectrum at each point every two seconds. The total acquisition time of an image is about 21 hours. The XRF spectrum of each pixel is processed with the PYMCA program developed by the synchrotron community  [12]. It allows to generate fitted peak area maps for different elements. The processing of an image takes about a quarter of an hour and produces semi-quantitative elemental maps for various elements (K, Ca, Mn, Fe, Cu, Au, Hg, Pb). The elemental distribution images are visualized using a home-made program called DataImaging 3D  [13].
Acknowledgements We thank  Rosaria Motta  and  Claudia Sindaco , in charge of the conservation treatment undertaken in the C2RMF studio from 2010 to 2013, for their precious observations and valuable discussions. We are grateful to  Thiphaine Fabris  and  Daniel Abriou  for the ion polishing preparation of the painting cross-sections at Saint-Gobain research EMC2 laboratory and  Patrice Lehuédé  for the SEM-FEG examinations. We warmly thank  Dominique Thiébaut  curator of the  Louvre museum for having initiated and encouraged the study of Giotto 's cross. We are indebted to Vincent Pomarède director of the Louvre painting department for his constant support.
Introduction The domestication of certain plants and animals at the beginning of the Holocene epoch beginning some 10,000 years ago heralded perhaps one of the most significant biocultural steps in the history of mankind. As a result, the study of the origins and spread of farming, through the palaeobotanical and zooarchaeological record provides the baseline datasets for understanding not only crucial aspects of complex evolutionary history of the species involved in their transition from wild to domesticated organisms, but also crucial biocultural evidence linked with the shift from hunting and gathering to early farming. Separating "wild" from "domestic" in the early zooarchaeological record is therefore one of the most important challenges facing researchers studying domestication, yet it remains one of the most difficult.
2006 , Rowley-Conwy et al. 2012)  even if an important overlap in size does exist between the two groups (e. g. Payne & Bull 1988 , Evin et al. 2013). Identification of zooarchaeological remains is often undertaken using a framework of "reference" individuals of known geographic origin and or wild/domestic status. To identify the biometrical affinity of Sus scrofa remains from archaeological sites in Europe, the most commonly used reference datasets are either modern Turkish wild boar  (Payne and Bull 1988)  or late Neolithic domestic pigs from the UK site of Durrington Walls  (Albarella and Payne 2005). These biometrical datasets are first and foremost limited both in their geographic and temporal extent and so their relevance or applicability to zooarchaeological collections from differeing times or places should be questioned.
Additionaly, a wild boar reference dataset should consist of more than a single population since wild boar are known to be very variable in size across their geographic range (e. g. ,  Groves, 1981; Albarella et al. , 2009; Rowley-Conwy et al 2012). More recently, studies have employed the more powerfull approach of geometric morphometrics to study morphological change in pig domestication (e. g. ,  Cucchi et al. 2009 , 2011 , Evin et al. 2013). In one study, molar size was shown to be a much poorer indicator of wild or domestic status in modern Sus scrofa than shape variables  (Evin et al. 2013). Indeed, the size of wild and domestic modern West Palaeactic pigs largely overlaps and does not show a bimodal distribution, which implies inevitable high classification error rates  (Payne & Bull 1988 , Evin et al.
From this perspective, this study aims to provide: 1) a new biometric framework for size measurments of modern domestic pig breeds and wild boars from a large geographic area, in order to provide descriptive statistics based on larger datasets than those already available; 2) statistically-controlled and more objective criteria to identify wild and domestic pigs using standard measurements of Maximum Tooth Length (MTL) and Maximum Tooth Width (MTW) on the 2 nd and 3 rd upper and lower molars. This approach relies on the definition of cut-off values that correspond to the optimal separation between the two groups based on a measure and visualisation of the error risk curves for erroneous identifications. In order to validate the identification-tool proposed, the results obtained were compared to the published measurements of the Sus specimens from the Late Neolithic site of Durrington Walls (Wiltshire, southern England), for which the measurements were published with the aim of being used as a standard of archaeological domestic pigs  (Albarella & Payne 2005).
Material The comparative specimens used in this study are the same as those in  Evin et al. (2013)   (2013). MTL and MTW were measured as the distance, automatically extracted, between the Cartesian coordinates of the most anterior and the most posterior semi-landmarks, and the most labial and lingual semi-landmarks, respectively. To confirm that the Estimated MTL (EMTL) and Estimated MTW (EMTW) are accurate estimates of the traditional measurements of the MTL and MTW, direct and estimated measures of lengths and widths (MTL-EMTL and MTW-EMTW) were compared for a subsample of 100 specimens based on pictures using TpsDig2 v2. 16  (Rohlf 2010). In their paper on the Neolithic pigs from Durrington Walls,  Albarella and Payne (2005)  published not only the summary of the measurements but also the full dataset, allowing direct comparisons with our results.
Cut-off values and error risk for identifying wild & domestic Sus The cut-off values separating modern wild and domestic pigs were estimated for each measurement and tooth following the protocol of  Favre et al. (2008)  and using the OpCut-Location v. 1. 0 IDL ® program developed by one of the authors (G. E. ;  Favre et al. 2008 ). The cut-off value was calculated from the means and standard deviations of the two a priori defined sets (wild boar and domestic pigs) of normally distributed variates. Normality of the groups were tested using the Shapiro-Wilk normality test with a type-I error threshold of α=0. 05. When a group was found to be not normally distributed, outliers were removed and normality restored. Outliers correspond to values above or below 1. 5 times the interquartile range  (Tukey 1977).
7%. Since a strong geographic variability exists in wild boar (e. g. ,  Albarella et al. , 2009; Rowley-Conwy et al 2012) , analyses were carried out for the full dataset, as well as for all specimens from Europe (France, Switzerland, Germany and Poland) and for Eastern populations (Iran, Iran, Turkey, and Russia), separatly. All statistical analyses other than those computed with OpCut-Location v. 1. 0 were performed using R v2. 13. 1 (R Development Core Team, 2011).
2012). The present study provides a new more extensive baseline dataset of modern comparative dental size measurements, and a more robust and rigorous statistical tool for use in identifying the domestic or wild status of Sus scrofa remains from archaeological sites. For the maximum tooth length measurements, the cut-off values established are 2. 39 cm for M 2 , 3. 69 cm for M 3 , 2. 26 cm for M 2 , 3. 79 cm for M 3 , whereas for maximum tooth width they are respectively 1. 85 cm, 2. 13 cm, 1. 50 cm, and 1. 75 cm respectively. Specimens with values below these cut-offs more likely correspond to domestic pigs, and above to wild boars. The cut-off values for maximum tooth length and width were measured and associated with curves and threshold values estimated for relative error risks of 1:2, 1:5, 1:10 and 1:100, corresponding to prediction of correct probabilities of ~67%, ~83%, ~91% and 99%, respectively.
2013). It is often quicker to measure linear distances than to acquire geometric data that require preliminary handling and treatments before the computation of statistical analyses. The effort and time required by both techniques has therefore to be considered in terms of the balance between questions asked and level of information required. It is clear that in terms of wider comparative zooarchaeological information, linear measurement datasets currently vastly outnumber those using Geometric Morphometric approaches. As a result, it is important to utilise these exiting datasets in new more systematic and quantitative ways, whilst at the same time being aware of and highlighting their limitations.
Conclusion This study provides a new biometric framework for distinguishing modern West Palearctic wild and domestic pigs that can be applied to existing biometrical datasets that incorporate linear measurements of molar teeth. The statistical tool presented in this study provides cutoff values paired with error risks and therefore offers more objective criteria for identifying wild and domestic pigs using simple measurements of maximum tooth length and width. This framework provides a much more extensive sampling of both modern wild boar populations and domestic breeds than has so far been available. The quantification of the error risk related to identification will allow researchers to revisit past and present biometrical datasets in order to more systematically assess the likley presence and proportions of wild and domestic Sus scrofa represented.