dataesr/scientific-paragraphs-categorization huggingface dataset:
VGGFace2 Dataset The VGGFace2 dataset contains 9000 identities. The distribution of faces for different subjects is varied, from 87 to 843, with a mean of 362 images for each subject. We did not perform experiments on the entire dataset for time reasons. We selected a subset of the dataset by randomly choosing 20 identities to assess the performance of our method. The selected subset consisted of eight women and twelve men, with a total of 7746 samples  [8].
ChokePoint Dataset The ChokePoint video dataset was designed for the identification of people acquired in real-world surveillance environments. The faces contained in the database have variations in terms of their pose, sharpness, and lighting, as well as misalignment due to the automatic detection/localization of faces. The Chokepoint video dataset consists of 25 identities (nineteen men and six women) in Portal 1 and 29 identities (twenty-three men and six women) in Portal 2. We used Portal 1 for our experiments  [9].
Evaluation 4. 2. 1. Quality of Generated Images In this paper, we trained a DCGAN model on two facial datasets, the LFW dataset  [7]  and VGGFace2 dataset  [8]. Figures  4  and 5  show samples of images generated by the DCGAN. As expected, the images appear realistic, although with occasional artifacts. More realistic images can be generated by increasing the number of epochs. In this experiment, the DCGAN produces images which are difficult to assess subjectively against real images. These augmented samples are very similar to the original images. The quality of the generated images improved over the course of 40 epochs. DCGANs represent a very effective tool to modify attributes of real faces such as gender, age, skin, and hair color, and allow for surprisingly realistic results. DCGAN-generated samples cannot be distinguished by the CNN discriminator in terms of whether they are real or not.
Thus, we assumed that DCGAN-generated samples would have similar CNN features, function as similar images, and help to create a limited dataset. Then, we applied basic manipulations on the synthetic images produced by the DCGAN. Figure  6  shows an example of the basic image manipulations applied to images generated by DCGAN. We added 100, 250, and 500 synthetic images per class to the LFW dataset  [7]  and VGGFace2 dataset  [8]. Our proposed method demonstrates the ability to synthesize realistic faces with DCGANs and basic manipulations, which can be used to improve face recognition tasks.
Results As shown in Table  1 , the more training samples are added, the higher the accuracy of the model is. The results show that our face recognition approach achieves an accuracy of 64% and 61% with the LFW dataset  [7]  and VGGFace2 dataset  [8] , respectively. After a period of collecting more samples, the accuracy increases to 90% and 88%, respectively. Furthermore, when adding 500 samples per class, we achieve 94. 5% accuracy on the LFW dataset  [7]  and 92. 2% on the VGGFace2 dataset  [8]. Table  2  shows that face recognition accuracy increases by 1. 7% when adding only 100 images per class using ChokePoint dataset  [9]. As expected, the face recognition accuracy increases as the amount of training samples increases. Table  1. Face recognition accuracy with data augmentation using the proposed method.
+0 +100 +250 +500 LFW  [7]  0. 64 0. 79 0. 90 0. 945 VGG  [8]  0. 61 0. 83 0. 88 0. 922 Table  2. Face recognition accuracy with data augmentation using the proposed method. +0 +100 ChokePoint dataset  [9]  94. 71% 96. 4% Tables  3  and 4  show the results of the experiments using 62 classes from the LFW dataset  [7]  and 20 classes from the VGGFace2 dataset  [8]. These experimental results demonstrate that our approach outperforms the traditional face recognition methods, namely, PCA, LBPH, and TRPCA. The results presented in Table  5 , obtained with ChokePoint dataset  [9] , confirm the effectiveness of our approach. Our proposed method based on DCGAN and basic manipulations for data augmentation and FaceNet + SVM for face recognition has more advantages than the PCA, TRPCA and LBPH methods, which use a smaller number of samples.
96. 4% Moreover, we can observe that our proposed approach achieves better results than the work of Pei et al. [63] , which was based on only basic data augmentation techniques (geometric transformations, brightness change, filtering and CNN for face recognition); see Tables  3  and 4. The number of augmented images did not change, at 500 images per face. The results shown in Table  5  confirm the effectiveness of our proposed approach on the ChokePoint dataset  [9]. Again, the number of augmented images did not change, at 100 images per face. Additionally, the experimental evaluation demonstrates that a significant increase in accuracy can be obtained by combining DCGANs and basic manipulations for data augmentation and FaceNet + SVM for face recognition compared to only basic manipulations (geometric transformations, brightness change, filtering.
) as a data augmentation technique (see Tables  3 4 5 ). The obtained results show that our proposed approach using a DCGAN with a filter operation for data augmentation achieves higher accuracy than our previous work  [6]  based on only DCGANs, with a difference of 1. 28% and 0. 04%, respectively, for the LFW dataset  [7]  and VGGFace2 dataset  [8]  and 0. 62% for the ChokePoint dataset  [9]. Moreover, the results show improvement with our proposed approach using DCGAN and basic manipulations (geometric transformations, brightness change) as a data augmentation method, with a difference of 2. 38% and 0. 38% over our previous proposed method  [6]  for the LFW dataset  [7]  and VGGFace2 dataset  [8] , respectively, and 1. 22% for the ChokePoint dataset  [9]. These results show that augmentations can, in general, considerably improve the quality of face recognition systems, and that the combination of generative and basic manipulations performs better than the other tested techniques.
Conclusions Researchers use data augmentation to increase the size of the datasets used to train deep learning models. In this paper, we demonstrate that the combination of DCGAN and basic manipulations can generate data that approximate real face images, thereby both providing a larger data set for the training of large neural networks and improving the generalization ability of recognition models. Based on the augmented human face dataset, facial features were extracted using FaceNet and then classified using SVM. The effectiveness of the proposed method was demonstrated by various experiments and comparisons with frequently used data augmentation and face recognition methods. The proposed data augmentation method generates images realistic enough to boost the performance of face recognition systems.
Although the combination of approaches used here for augmentation demonstrates a good increase in accuracy, the basic approach is not far behind in terms of performance while requiring less time and hardware resources. Improving the quality of DCGANgenerated samples and evaluating their effectiveness on a broad range of datasets is a very important area for future work. DCGANs can be optimized by adjusting parameters such as batch size, momentum, and learning rate in order to generate more realistic and diverse face samples, which could further improve the accuracy of the results. Future work could include the use of Wasserstein loss with a gradient penalty to improve the quality of the generated images.
Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: computational neuroscience; dendritic computations I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000.
Acknowledgements I used "we" as science is a collective endeavour. Discussions on this topic had begun as early as 2013 with my former PhD Advisor and collaborators from  Institut Pasteur Paris. I also want to acknowledge  M. Humphries ,  F Zeldenrust ,  A. Foust  for their valuable comments on the early draft and  Ms Marini-Audouard  for the proof-reading before submission.
In France, flooding is the most common and damaging natural hazard. Global warming is expected to exacerbate flood risk and could be more pronounced in the European Alps which are experiencing a high warming rate, likely to lead to heavier rainfall events. Alpine valleys are densely populated, potentially increasing exposure and vulnerability to flood hazard. The study of historical records is highly relevant to understand long-term flood occurrence and related socio-economic impacts in relation to changes in the flood risk components (i. e. hazard, exposure and vulnerability). To this aim we introduce the newly constituted database of Historical Impacts of Floods in the Arve Valley (HIFAVa) located in French Northern Alps starting in 1850. This quite unique database reports historical impacts related to flood occurrences in a well-documented Alpine catchment that encompasses both hydrological and socio-economical diversity.
After a complete description of the database (collection, content and structure), we explore the distribution of the recorded impacts with respect to their characteristics and evolution in both time and space. The analysis reveals that small mountain streams and particularly glacial streams caused more impacts (67%) than the main river. While an increase in heavy rainfall and ice melt are expected to enhance flood hazard in small Alpine catchments, this finding calls for greater attention to flood risk assessment and management in small catchments. The analysis also reveals an increasing occurrence of impacts from 1920 onwards, for which possible factors are discussed. Further work is, however, needed to conclude on the respective contribution of the source effect, the increase in flood hazard or the exposure of goods and people.
e. have left a "social signature". Those high impact events can come close to the notion of a catastrophe, as they can lead to societal upheaval  (Soanes and Stevenson, 2009)  sometimes deleterious but also beneficial (behavioral change promoting prevention)  (Garnier, 2017). High impact events are by nature rare, often resulting in a lack of available data (e. g. description of the event, time, extent, damages caused etc. ). However, historical analysis allows a social and spatial-temporal contextualization of the data  (Giacona et al. , 2017) , making the reconstruction (date, impacts) of major flood events possible  (Barriendos et al. , 2003 (Barriendos et al. , , 2019) )  and attesting the social apprehension of the phenomenon  (Gil-Guirado et al. , 2016). Numerous historical databases were built to document past flood occurrence and magnitude, such as the Prediflood database  (Barriendos et al.
, 2014) , and some, as the database from  Thoumas (2019) , allow to analyze the climatic fluctuations. In contrast to these latter databases focusing on hydrological events, some databases gather information on the socioeconomic impacts of floods such as the APAT database  (Lastoria et al. , 2006) , the press database on natural hazards and climate change from  Llasat et al. (2009) , the database of high-impact weather events in Greece from  Papagiannaki et al. (2013) , the EUFF database  (Petrucci et al. , 2019)  and the SMC-Flood database  (Gil-Guirado et al. , 2019). Some databases stand out as the participative flood database ORRION  (Giacona et al. , 2019) , the on-line information resources of the Chronology of British Hydrological Events  (Black and Law, 2004)  or the database built by the RISC-KIT project  (Garnier et al.
, 2018)  which aim at developing methods, management approaches and explore trajectories of vulnerability. Floods, as natural hazards, are physical phenomena naturally occurring and can, when certain conditions are met, cause harm to societies. They can be interpreted as a social construction  (Beck, 1992)  since exposure of human activities and social vulnerability play a large role in the severity of the impacts. Flood impacts databases, constituted from historical records, can be considered as the expression of society's concerns, risk perceptions (fear, habit) and values (based on reported impacts). The recording of flood impacts, or the failure to record them, provides a subjective measure of the events that were considered worth reporting for various reasons across historical periods.
, 2016). The present paper introduces a newly constituted database of flood impacts of the Arve River and its tributaries (Northern French Alps). The database called "Historical Impacts of Floods in the Arve Valley" (HIFAVa) covers all impacts caused by hydrological events that occurred since 1850. The study of this database, probably the first one documenting flood impacts over historical time scale in a mountainous catchment, ultimately aims at analyzing the interactions between social and natural dynamics engendering these impacts. In this paper we present the dataset and first results of the impacts analysis with respect to their nature and evolution in both time and space.
3. 1. Collecting data from historical archives. Mélo et al. (2015)  conducted historic researches to identify a flood chronology and hydro-meteorological circumstances of the flooding events which happened in the Arve catchment between the 18 th and the 21 th century. As sources are more abundant and richer in information over the last 165 years , this period was defined as the studied time frame of the HIFAVa database. Only events that triggered impacts and that were mentioned in at least two sources were integrated in the database. Since 2015, data have been further collected to complete the preliminary dataset from  Mélo et al. (2015). Information on flood related impacts were collected from various sources. Primary sources range from handwritten archives like municipal acts to departmental archives (e.
g. reports of the Préfecture and of town councils). Secondary and tertiary sources are respectively made of published documents (newspapers, reports, books) and pre-existing databases. The database of historical records providing a chronological and synthetic layout of the data is composed of (Figure  2  and Table  A1 ): Manuscript materials: -Most of the manuscripts are kept in the departemental archives (Archives Départementales de Haute-Savoie: ADHS) (Conseil départemental de la Haute-Savoie, n. d. ) or in the municipal archives (Archives municipals : AM) of the towns of Chamonix, Cluses and Bonneville. The departmental and municipal archives also contain records older than the Savoy annexation by France in 1860. The records can be private or from a public institution. Printed materials: -Articles from scientific journals and books used in this study have been published since 1914.
Newspapers describe the causes and consequences of the flood events and sometimes provide instrumental data and illustrations. They also contain information concerning the public authority response and past discussions. Other records: -The national database of historical flooding (Base de Données Historiques sur les Inondations : BDHI) gathering floods events considered as "remarkable" in the French territory (Ministère de la Transition Ecologique, n. d. ;  Boudou, 2015)  was also used. -The database created by the department of Restoration of Mountainous Areas (Restauration des Terrains en Montagne : RTM) from the public institution managing the French public forests (Office National des Forêts : ONF). The ONF-RTM database gathers transcriptions of observations of the RTM officials as well as information collected from diverse primary sources.
These data (labelled RTM in the HIFAVa database) are freely available through the open-access and online ONF-RTM database  (RTM and ONF, 2012). Some specific ONF-RTM reports are also included in the HIFAVa. The RTM database was built to assist the management of small tributaries. -A movie realized in 1990 by the RTM is also mentioned as a source. -Some records are from the Syndicat Mixte d'Aménagement de l'Arve et de ses Affluents (SM3A), which is the institution in charge of the management of the Arve River and its tributaries since 1994.
In other words, sometimes numerous impacts caused by a single flood event are registered under distinct ID because it was possible to localize each impact precisely (at the hamlet scale). Sometimes we can only localize the impacts at the municipality scale, meaning that all impacts are registered under the same ID. The severity of an event can not be estimated by the number of ID registered in the database. The most recent sources are often highly informative, allowing impacts to be more precisely located. Impacts occurring on the same day on a given river are expected to be caused by the same flood event. As a result, the date is the key used to connect each impact to a flood event. This "flood event" definition has been extrapolated to impacts occurring on the same day in different catchments, assuming that two impacts occurring the same day can be caused by the same hydrometeorological event given the moderate surface area of the Arve catchment.
3. 3. Text mining. The flood impacts of the HIFAVa were categorized through a text quantitative content analysis with the KH Coder software  (Higuchi, 2015). The description of the impacts comes from comments contained in the records. The most frequent words were gathered in order to determine representative categories of the database content. A category is made of several words assigned to a coding rule. Categories were inspired by the flash flood impact severity scale of  Diakakis (2020). This analysis led to the following seven categories with example of the assigned words: -Transport network: e. g. "road", "bridge", "railway", "street". -Urbanized areas and residential buildings: e. g. "house", "town", "basement". -Natural environment: e. g. "forest", "field", "yield", "sediment transport".
-Protection infrastructures and dams: "dikes" and "dam". -Industrial, commercial and recreational facilities: e. g. "mill", "factories", "golf", "camping", "hotel", "school". -Critical installations: "drain", "power transformer". -Victims: "dead", "injured", "missing", "evacuee". Table  1. Extract of the HIFAVa database showing its structure and examples of content. Refer to the Online Resource 2 for details about the column contents.
4. 1. Evolving sources over time. During the studied period, the diversity and the quantity of sources in which mentions of impacts were found fluctuate (Figure  2 ). Among the existing databases used, the BDHI database for instance continuously covers the studied period but was sporadically informative since it only contains two mentions of impact events in the Arve catchment, respectively of the Bon Nant River in 1892 and of the Borne River in 1987 (Figure  2  One of the evolutions of the sources is the increase in newspaper articles mentioning flood impacts. Following the 1881 press freedom French law, the 1880-1889 decade marks the emergence of articles recording natural hazards, such as flood impacts  (Ferenczi, 1996). However, the 1855 flood in Bonneville was already reported by the swiss newspaper, Le Journal de Genève.
Following the floods of the Rhône, the Loire and the Garonne in 1854, the 1858 law against urban flooding places flood control at the heart of the national legislation for the first time. In the following, the RTM department was formed for the reforestation of mountains slope in order to prevent the reproduction of major floods. The department became quickly efficient and since 1860 collected numerous reports. Built for the study and management of small tributaries, the RTM database became the main source of information since 1930 for the HIFAVa database. Hence, the strong emergence of the RTM source among the others may play a role in the observed increase in impacts since 1920, even more noticeable since 1960. The rise in the number of impacts per flood may be partly explained by the fact that distinct impact types in the same location were reported and therefore referenced under distinct impact ID, while they were not differentiated in previous periods.
Recent sources seem to provide more accurate information on the impacts and their locations. In older sources, impacts are most of the time documented at the city scale (21% of the impacts for the first period, and 10% for the second period). Thereby, all these impacts are stored in the database in a single line with an uncertainty code for the impact location corresponding to the municipal level. In most recent sources, impacts' locations are described more accurately allowing them to record at a resolution up to the building scale. As a result, impacts are stored in as many lines as impacts locations can be identified, with an uncertainty code for the impact's location corresponding to the building or neighborhood level (85% of the impacts for the second period). For example, in 1996 fifty-three impacts where recorded for the same event and fifty of them where located in distinct places in Chamonix.
The rise in impacts can also be due to numerous impacts in different locations, as the flood of 1990 which impacted six towns in two different sub-catchments (the Arve and the Giffre catchments). However, in order to overcome the bias induced by the recording of impacts according to their location, we aggregated the impacts at the municipality level. That is to say, all the impacts reported for a given municipality that were caused by the same event (thus the same day) are recorded under the same line in the database. This results in 562 "aggregated" impacts instead of 916 impacts initially. From these data, we redrawn Figure  3  (Figure  A2 ) comparing the moving average of impacts and associated events. We can see that in both figures the trends of increasing impacts are similar. There is an increase of impacts (here starting soon as 1890s).
Thus, the way the impacts are stored in the database (by location or by municipality) affect the absolute values of impact per year but not the observed temporal changes over time. Changes in exposure and vulnerability related to land use is another potential explanation of the rise in the number of impacts  (Magnan et al. , 2012; Garnier and Desarthe, 2013; Camuffo et al. , 2020). In fact, as major population growth happened, especially in Bonneville and Chamonix, and lead to a strong and fast urban sprawl in the flood plain between the 1950's and the 2010's as shown by aerial photographs (Figure  6 ). They also show the vanishing of the alluvial forest  (Dufour and Piégay, 2006)  and cultivated fields to the benefit of urbanization in both towns. Upstream, in Chamonix, the demographic expansion dates back to the early 20 th century with the flourish of mountain tourism.