dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019)  and replaced all the emojis with their detailed descriptions 11. Replacing URLs and emojis improved the results for all the models we have tested, so we give here only the results obtained after these replacements. BERT R own emb + base. Following  (Parikh et al. , 2019) , we also experiment stacking multiple embeddings. We tailored a pre-trained BERT model 12 for which we used the whole non annotated dataset (i. e. , 205, 000 tweets). The original BERT model uses a WordPiece tokenizer, which is not available in OpenSource. Instead, we used a SentencePiece 13 tokenizer in unigram mode. Training the model using the Google Cloud infrastructure with the default parameters for 1 million steps took approximately 3 days. BERT R features. We relied on state of the art features that have shown to be useful for the task of hate speech detection: Surface features (tweet length in words, the presence of personal pronoun and third-person pronoun, punctuation marks, URLs, images, hashtags, @userMentions and the number of words written in capital), Emoji features 11 (number of positive and negative emojis), Opinion features (number of positive, negative and neutral words in each tweet relying on opinion  (Benamara et al.
Conclusion In this paper, we have presented the first approach to detect reports/denunciations of sexism from real sexist content that are directly addressed to a target or describes a target. We proposed a new dataset of about 12, 000 French tweets annotated according to a new characterization of sexist content inspired from both speech act theory and discourse studies in gender. We then experimented with several deep learning models in binary, three classes and a cascade classifier configurations, showing that BERT trained on word embeddings, linguistic features and generalization strategies (i. e. , place and hypernym replacements) achieved the best results for all the configurations, and that cascade classification allows to successfully correct misclassified non-sexist messages. These results are encouraging and demonstrate that detecting reporting assertions of sexism is possible, which is a first step towards automatic offensive content moderation.
The contribution of vapor of continental origin, produced either by plant transpiration or soil water evaporation, should additionally affect the 17 O-excess signature of precipitation, although no data are available so far to evaluate the magnitude of this process. For the study presented here, we collected precipitation from two sampling stations, both located in Benin and affected by a similar oceanic moisture source in the Gulf of Guinea. The first station (lat. 6°26' N; long. 2°21' E) is located along the coast and is essentially subject to oceanic influence. The second station (lat. 9°44' N; long. 1°34' E) is located 400 km inland and may be additionally affected by continental vapor recycling. The stable isotope composition of rainfall samples (δ 2 H, δ 18 O and δ 17 O) are measured on a WS-CRDS Picarro L2140-i, using three replicates per sample.
The objective of this study was to test the feasibility of automatically extracting and exploiting data from the YouTube platform, with a focus on the videos produced by the French YouTuber HugoDécrypte during COVID-19 quarantine in France. For this, we used the YouTube API, which allows the automatic collection of data and meta-data of videos. We have identified the main topics addressed in the comments of the videos and assessed their polarity. Our results provide insights on topics trends over the course of the quarantine and highlight users sentiment towards on-going events. The method can be expanded to large video sets to automatically analyse high amount of user-produced data.
Introduction YouTube is an online video-sharing platform, used by individuals, professionals or institutions. It provides a huge range of Health-Related Content and was used during COVID-19 outbreak as a source of information  [1] [2] [3]. In most of the studies using YouTube, videos are watched and then analysed manually  [4]. However, there exist automatic methods to extract useful information from online content, and in particular YouTube  [5]. The objective of this study is to test the feasibility of automatically extracting and exploiting data from YouTube. For this, we will seek to identify the main topics addressed in the videos of a French well-known Youtuber, and the audience's reactions to these topics.
Material and Methods A YouTube video is characterized by a title, an author, the video itself, subtitles, a number of positive/negative rates (like/dislike), comments. In this work, we analysed the YouTube channel HugoDécrypte followed by more than 868 000 subscribers  [6]. We collected and exploited a maximum of data from videos of the "daily news" playlist released between March 17 th 2020 and May 04 th 2020, as it provides a chronological overview of daily news regarding the COVID-19 outbreak in France. YouTube Data API provides a way to collect metadata of a video: title, author, duration, publication date, number of like/dislike, number of views, number of comments, text description or its comments: author, publication date, text, number of like/dislike. The API can be implemented in Python.
Some of the metrics retrieved by the API, namely number of views or like/dislike count, are representing a snapshot of current data at the time of the API call and thus cannot be used retroactively. Retrieval of number of views of a video overtime necessitates data collection stream. Thus, for the present study, data used were the video title, publication date, comments publication date and text. In order to identify the main topics addressed in the videos, we have implemented the following steps. (i) We extracted comments published in the 24 hours following the release of the videos. (ii) For each video, we detected the 10 most frequent words from which topics were drawn. Data management followed Natural Language Processing (NLP) steps: accent and stopwords removal, tokenization, stemming.
(iii) Topic cooccurrence was assessed by computing the frequency of comments sharing both topics. The two most co-occurring topics were linked in the network graph as well as the topics presenting at least half the co-occurring frequency. (iv) Data from all the videos were pooled to analyse the most discussed topics globally and compute the relative daily frequency of comments for each topic. To evaluate the audience's reactions to these topics, we used the polarity score obtained using SAS Viya VisualTextAnalytics software  [7]. This method is using a sentiment lexicon attributing polarity scores to individual words. The overall comment score is then computed based on each word polarity score as well as sentence structure, punctuation and emoticons, on a scale of -1 (extremely negative) to +1 (extremely positive).
Even if the YouTube API provides an easy way to automatically extract data from the YouTube platform, it also presents some limitations. First, all data are not available: subtitles can only be extracted by the owner of the channel. Secondly, when submitting retrospective queries, the API returns a limited amount of content. In order to have completeness, the query may be submitted in real time in streaming. Furthermore, the YouTube API is returning a non-exhaustive sample of the videos and comments that may vary from a query to another and based on the time between query and publication date. Last, the NLP methods for the treatment of comments used in this study delivered decent results with the selected videos, but it depends very much on the community, the vocabulary and language used as well as the topics discussed.
This has yet to be tested in other contexts. Some parts of the extraction and cleaning process of the video content may depend of the context and need to be updated for each study. Authors have to be cautious when interpreting results from polarity score. Indeed, we experimented with another French lexicon besides SAS sentiment analysis, from the library TextBlob_fr  [8] , which returned different raw polarity score. The first method is biased towards negative score while the second method is biased towards positive score. Sentiment analysis studies on French content is lacking compared to English corpus. The development of a more complete and up to date lexicon for French content, especially focused on social network corpus, is required to improve the results and reliability. To go further, emotion analysis (happy, sad, angry, fearful, excited, bored) can also enhance results by providing a more precise picture of the community feeling towards the different topics  [9].
2007) , were extracted during a 5-year period (2012-2016) at a 4-km resolution from the Geostationary Operational Environmental Satellites and the Polar-orbiting Operational Environmental Satellites (GOES-POES) dataset and the Moderate Resolution Imaging Spectro-radiometer (MODIS; http://oceanwatch. pifsc. noaa. gov/). For each reef, we calculated the weekly average of temperature and ocean primary productivity over a 5-year period (2012-2016) but we did not account for seasonality. Using a set of six reef habitat and environmental variables, we first performed a principal components analysis (PCA) to describe similarities between our ecological sites while dealing with multicollinearity and we included depth, weekly average SST and primary productivity, reef complexity, percent cover of macroalgae, and live hard coral  (Cinner et al.
Travel time to markets For each reef, we considered two human settlements: (i) the nearest coastal community; and (ii) the nearest market. Travel time was estimated as the shortest time of travelling between each reef and its nearest market and community, respectively, considering the following:. • Sea travel using wooden pirogue canoes: speed of 7 km. h -. • Primary roads using motorized vehicles: speed of 50 km. We also assessed the total human population occurring within a 4-km radius of each reef using the WorldPop dataset version 2. 0 (Tatem 2017), which estimates human population with national adjustments at a 100-m resolution for the year 2015 (the year closest to 2016, year in which the ecological and social surveys were conducted). We set the cut-off at 4 km to reduce spatial overlap between reefs.
2016, / prohibited Cinner et al. 2018 Environment Sea surface temperature Weekly average SST over 2012-2016 Williams et al. 2015 Reduced as two PCA (SST) extracted from GOES-POES 4-km components and standardized resolution dataset Environment Primary productivity Weekly average chlorophyll-a Williams et al. 2015 Reduced as two PCA concentration over 2012-2016 extracted components and standardized from MODIS-Aqua 4-km resolution dataset Habitat Depth Mean depth along the 50-m transect Srinivasan 2003, Brokovich Reduced as two PCA et al. 2008 components and standardized Habitat % Live hard coral Sum of the percentage of the Luckhurst and Luckhurst Reduced as two PCA corresponding category within the 200 1978, Roberts and Ormond components and standardized points along the 50-m transect. Estimated 1987, Bellwood et al.
Acknowledgments We would like to thank all attendees who contributed to the HOME VII meeting and were active partners in helping to achieve international consensus in completing the  HOME Core Outcome Set  (Table S1; see Supporting Information). Particular thanks to the local organizing committee:  Norito Katoh ,  Masutaka Furue ,  Hidehisa Saeki  and  Takeshi Nakahara  and  Convention Academia , to  Masaki Futumura  for supporting our patient partners during the meeting and running the patient session, and to our patient contributors:  Jennifer Austin ,  Julie Block ,  Alix Bullock ,  Tim Burton ,  Lynita Howie ,  Rosemary Humphreys ,  Yuko Ikegami ,  Henrique Ishii ,  Michael Lanigan ,  Kyoko Maru ,  Eri Maruyama ,  Miwako Ogino ,  Vanessa Sultana  and  Akane Yasui. Yukiyasu Arakawa  and  Susumu Ichiyama  translated for Japanese patient representatives.
Analysis of additional data: Identification with the marketing profession and long-term branding orientation A key conceptual idea in our study is that identification with marketing profession is linked to prioritizing long-term branding goals over short-term revenue growth. To validate this idea, we collected additional data from 47 employees in professional service environments in Germany (mostly in management consulting). We chose this target group because we expected that marketing identity would vary substantially within this group. To measure marketing identification, we used the same item described above (but employing a seven-point scale). We classified participants as having a high identification to the marketing profession if they responded with 4, 5, 6, or 7 on the identity scale, resulting in 14 participants.
, 2007)  (Appendix C). As we measured internal accountability only in the given situation, two items dealing specifically with felt accountability in the respondent organization were eliminated. Each item retained referred to the situation presented, for which the respondent assessed his/her feelings-for example, "In this situation, I will have to explain my decision to management. " Preliminary data were collected from a sample of 145 students in business management, based on another managerial scenario more suited to a student audience. An exploratory factor analysis resulted in the emergence of a two-factor structure, more reliable than a single-factor structure. Although initially considered as unidimensional by the authors, the scale reveals two dimensions: felt accountability to management and felt accountability to other colleagues.
98) and visiting intentions (α =. 98) in the prior studies, as well as their willingness to pay for a tour of the library. Lastly, participants responded to covariate items, controlling for mood between data collection times (1 = very bad; 7 = very good), passport ownership, prior visits to France (15% indicated yes), attitude toward France (1= hate France; 7= love France), and number of vacation days per year. Participants then completed demographic information. To check the manipulation of the cultural-relevancy prime, participants indicated the extent to which the library featured in the advertisement was connected to the United States.
Approaches of geographic ontologies can help to overcome the problems of ambiguity and uncertainty of remote sensing data analysis for modeling the landscapes as a multidimensional geographic object of research. Image analysis based on the geographic ontologies allows to recognize the elementary characteristics of the alas landscapes and their complexity. The methodology developed includes three levels of geographic object recognition: (1) the landscape land cover classification using Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) classifiers; (2) the object-based image analysis (OBIA) used for the identification of alas landscape objects according to their morphologic structures using the Decision Tree Learning algorithm; (3) alas landscape's identification and categorization integrating vegetation objects, territorial organizations, and human cognitive knowledge reflected on the geo-linguistic object-oriented database made in Central Yakutia.
g. , vegetation, moisture, landform (relief), climate conditions, soil  (Pashkevich, 2017) ); (b) morphologic forms and geometric objects characteristics (e. g. , shape, size, textures, topology, geolocation, depression, inclination, slope), and (c) anthropogenic objects related to the land field reality measured (captured) by remote sensing, the human geographic object's identification (semantic meaning) and perceptions. The human geographic object's identification (semantic meaning) was formalized in Russian and Yakutsk languages in a geolinguistic database integrating toponyms, territorial knowledge of local  population, census, land uses, maps, etc. (Zamorshikova et al, 2018). This geo-linguistic database including the bunch of heterogeneous knowledge poses the problem of standardization (harmonization).
According to it, the cultural, social and territorial significance of the alas is defined  (Desyatkin et al, 2018). Therefore, the modeling of the alas landscape needs to integrate both the human semantic factors and the geo-physical reality, associating quantitative and semantic parameters defining ontologies of the alas geographic space. The approach for recognizing the geographic ontologies merges manually collected data from the geographic landscape (field observations, population knowledge formalized in geo-linguistic database), maps and remote sensing data: Landsat series, 5 TM, 7 ETM+, 8 OLI and Sentinel 2. Geographic object ontologies are extracted from the satellite images by semi-automatic or automatic approach of image analysis  (Clouard et al, 2013)  using machine learning algorithms Spectral Angle Mapper (SAM) on the Landsat series images according to the better performance (overall accuracy).
Methodology The geographic objects as ontological elements are the basis of the structure and organization of a landscape  (Gadal, 2012). In remote sensing they are identified by image processing and image analysis (here by machine learning, landscape object classifications, and OBIA image segmentation) with the knowledge GIS database and the implementation of ontological rules (Fig. 2 ). Figure  2. Implementation of image analysis with geographic ontologies for alas landscape recognition. The typical alas landscapes consist of the following elementary geographic objects: lake, grassland, woodland, built-up and arable lands for agriculture. First, they are identified and analyzed according to the land cover classes produced from the biophysical parameters measured spectrally by Landsat series and Sentinel 2 data.
The second level of the geographical objects recognition by object-oriented classification algorithms of DT is resulted from knowledge acquisition  (Clouard et al, 2010)  structured in the geo-linguistic database and integrated in GIS, and the OBIA to extract the morphology structures of the alas landscape. The categorization of the alas landscapes objects generated is followed-on by the fusion of land cover maps and alas landscape object classification. The data analyzed include the expert and the domain knowledge (landscape theories, concepts, indicative and cognitive analysis products)  (Gadal and Ouerghemmi, 2019). The ontological approach enables us to obtain numerous representations of all aspects of the alas landscape spatial organization through the accurate acquired knowledge structured in the GIS database.
It is used both for training, learning and the validation of results, the spatial analysis and modeling in GIS. The integrity and the logic coherence of all processes taking place in geographic space is ensured by the integration of semantic geographic object ontologies modelled by the geostatistical analysis of geolinguistics database (toponyms, territorial knowledge of local population, census, land uses, maps, etc. ) with the categorization of alas landscape based on GIS knowledge database. The obtained alas landscape model (Fig. 3 ) is a multidimensional model including knowledge and geo-linguistic GIS database adapted for machine learning training and simulation of geospatial processes: rural exodus, permafrost degradation, etc. having impact on the landscape's changes and their consequences (sustainability, re-greening).
Recognition of objects of landscape land cover change The recognition of the landscape objects and the change dynamics modeling between 2007 and 2019 is based on the use of the Landsat 5 TM (date: 15. 07. 2007), Landsat 8 OLI (bands 2 to 7 : VIR-NIR-SWIR) pansharpened at 15m (date: 26. 07. 2013) and Sentinel 2A (bands 2 to 5 : VIR-NIR at 10m) (date: 24. 07. 2019) acquired in July. Landscape land covers produced and mapped by supervised classification are used as training data of the GIS database. Landscape objects are recognized by the spectral signatures related to the geographic objects of the GIS database. Several classifier algorithms were tested: Minimum Distance (MD), Maximum Likehood(ML), the K-Nearest Neighbors (K-NN), Spectral Angle Mapper (SAM) and Support Vector Machine (SVM). The results show that the optimal machine learning classifiers are obtained with SAM for the Landsat 5 TM and the Landsat 8 OLI data confirming the results presented by Pertopoulos et al.
6c ). The average spatial neighborhood radius is 25 meters. alas and thermokarstic basins concern their shapes: alas has a shape close to the circle, does not have distinct angles, and is not characterized by elongation contrary to thermokarstic shapes. (d) The fourth phase consists in separating the alas from another anthropogenic objects: agricultural parcels. The identification methodology uses a decision tree algorithm. This machine learning algorithm required a dataset that selected 30 polygons generated using the GIS database, more specifically the alas toponyms, topographic maps and Open Street Map data. The classification structure defined by the decision tree is estimated from the training data using a statistical procedure of overall mean and standard deviation for 3 PCA components.
The decision rules developed are used to associate each segment of the image with one of the geographic object classes. The O. A. with Sentinel 2A data is =88. 4%; a Kappa coefficient is 0,83 for the Decision Tree classifier. The result is two broad types of geographic objects in which elementary ontological and geographic structures are reflected: alas and arable lands of agriculture (Fig. 7 ). The information was obtained through the interpretation of remote sensing data by artificial intelligence with the integration of expert knowledge for learning and validation of the obtained results. This GIS-structured information set provides the basis for a comprehensive spatial analysis using semantic ontology to model the landscape dynamics of the boreal forests of alas and permafrost.
ALAS LANDSCAPE MODEL-ING In this part, some applications of the image analysis data obtained in the modeling of the alas landscapes are presented. The ecological status of the alas landscapes plays an important socio-economic role, as well as in the dynamic processes of the permafrost. The results of spatial remote sensing image analysis using GIS-integrated geographic ontologies are designed to serve as a basis and environment for this research. Thus, the modeling of geo-linguistic landscape features has been developed in GIS with the creation of an extensive geographically referenced database. It aims to understand better the land use and to measure the complexity of landscape structure. The first method consists of performing modeling analysis land covering changes within alas. The four categories were identified according to the structure of alas vegetation: (a) Termokarst lake, when the alas basin is completely filled with water; (b) Complex alas, when vegetation changes from steppe to coastalaquatic; (c) herbaceous alas, when vegetation is relatively uniform throughout all alas territories; (d) pasture alas, with active arable or hay areas.
The novelty of the research comprises the integration of the geo-linguistic database reflecting the semantic meaning of people's knowledge as part of the geographic ontology of alas landscapes. It allows to analyze the value, the age and the evolution of alas increasing the accuracy of recognition of alas objects. One of the main challenges is to hybridize geographical knowledge with artificial intelligence used to analyze the landscapes and the cross processes of anthropization and geophysical evolution of permafrost by spatial and airborne remote sensing. (ANR-14-CE22-0015), the Russian Science Founda-.