dataesr/scientific-paragraphs-categorization huggingface dataset:
Given that our model was optimized on avatar faces, comparing our model's prediction to real participants ratings in a second step allows us to assess whether our model would give overall coherent ratings with those of real human beings. Our first analysis confirmed the significant correlation of the modeled trustworthiness and dominance estimates with the actual participants' ratings of trustworthiness and dominance ratings on the faces from these databases (except the FEI Face database which did not provide subjective ratings; Supplementary Figure  3 ). We found significant correlations for both trustworthiness and dominance estimates (trustworthiness: r = 0. 22, p < 0. 001, dominance: r = 0. 16, p < 0. 001-N = 768 for each correlation, to not artificially increase the statistical power of this analysis only the neutral and facing version of the faces were used for these correlations), confirming that our model gave trustworthiness and dominance estimates that are coherent with real participants' evaluations on these traits.
59, p = 0. 009; Supplementary Fig. 5E ). Testing the relationship between interpersonal trust and portrait Selfies' trustworthiness. We tested whether displayed trustworthiness could be used as a proxy for interpersonal trust. To do so, we analyzed the Selfiecity database  29  which includes 3230 selfies posted on Instagram in 2013 from six cities across the world (Bangkok, Berlin, London, Moscow, New York and Sao Paulo; analyzable images: N = 2277  29  ). The identified faces were then individually analyzed by two independent raters who were asked to evaluate, for each picture, the alignment of the OpenFace's face identification points compared to the real face's contours (coded as 0 or 1). The sum of these goodness of fit was then used as weights for the analyses. Therefore, only faces for which the two raters agreed that they were not well detected were removed from the analyses.
6C ) and the sum of five questions bearing on cooperation ('how acceptable is claiming government benefits', 'avoiding a fare on public transport', 'cheating on taxes, keeping money that you have found', 'failing to report damage you've done accidentally to a parked vehicule'; Supplementary Fig. 6D ). As the Selfiecity database is constituted of pictures posted online in 2013, for each country, the most recent vague of the European or World Value Survey was taken (i. e. , 2008 for Russia, 2009 for Great Britain, 2011 for the United States, 2013 for Thailand and Germany, and 2014 for Brazil). In line with our hypotheses, people located in places where interpersonal trust and cooperation are higher, displayed higher levels of trustworthiness in their selfies (cooperation level: b = 0. 13 ± 0.
In order to keep a maximal temporal resolution, missing values in the GDP per capita and Polity2 indices were completed using the closest previous value, except for the time-lag analyses in which no imputation was made. A total of 1943 data points were included in the analyses looking at the effect of GDP per capita. A total of 1115 data points were included in the analyses looking at the effect of Polity2. Paintings were analyzed using individual linear models (each painting corresponding to one data point), taking the sitter's gender, age and level of dominance as control variables. Bayes factor analyses were conducted using the BIC approximation, which approximates Bayes factors computed under the unit information prior  63. Finally, time-lag analyses were conducted to analyze the temporal dynamics between trustworthiness, GDP per capita and democratization.
Web gallery of art. Data from the Web Gallery of Art (WGA) were analyzed in a similar way as the paintings from the NPG. To better account that the portraits actually reflected the sitter's willingness to display trustworthiness traits, paintings were geocoded using the painter's place of activity at the time of the painting. This geo-coding resulted in 19 countries with paintings ranging from 1360 to 1918. As previously, two independent raters categorized the quality of detection of the faces and these evaluations were used as weights in the linear regression (number of analyzed portraits: N = 4106). As for the National Portrait Gallery, the missing levels of affluence and democratization were completed using the previous complete value. The same models as previously were used except that a random effect was included to take the localization of the paintings into account.
Acknowledgements We are grateful to the  National Portrait Gallery  and to the  Web Gallery of Art  for allowing open access to high-quality paintings online, to  Prof. Alexander Todorov  for the distribution of the avatars controlled for dominance and trustworthiness as well as to  Dr. Lev Manovich  and the  Selfiecity team  for allowing the use of their database. We would like to thank  Dr. Tadas Baltrusaitis  for the creation and free distribution of OpenFace. We would like to thank  Anis  for his feedback on the construction of our algorithm. We would like to thank  Loïa Lamarque ,  Paul Grignon  and  Benoît de Courson  for their help in coding OpenFace goodness of fit of the portraits. We would like to thank  Prof. Alexander Todorov ,  Dr. Malgorzata Mikucka ,  Dr. Jeffrey M. Girard  and an anonymous reviewer for their insightful comments on our manuscript.
0327) (Table  1 ). Hence, at any significance level, this test rejects the null hypothesis of independence between conditions and chills frequency and intensity. We then considered the physiological data from the camera and cardiac sensors. Some of the physiological data was lost after collection and the remaining data did not reveal any significant difference across groups. We first examined the camera data for the remaining sample (N = 11), and found a higher count of smiles in the recordings for the participants wearing the device (M = 1. 18; STD = 1. 78) than participants without (M = 0. 27; STD = 0. 65). A pairwise two-sided Wilcoxon test revealed that this difference is not significant (V = 51, p = 0. 3804). We then analysed the heart rate data for the remaining sample (N = 12), and found an increase in the LF/HF ratio for participants with the device (M = 2.
0147). We also analyzed reports of cognitive empathy (i. e. , shared viewpoints). Participants wearing the device reported sharing the speaker's viewpoint (M = 8. 33; STD = 1. 15) more than when viewing the stimulus without the device (M = 8. 09; STD = 1. 79). However, a pairwise one-sided Wilcoxon test revealed the difference to be not significant (V = 18. 5, p = 0. 5). Participants with the device also reported a greater amount of pleasure in the viewing experience (M = 7. 66; SD = 1. 28) than those without the device (M = 6. 95; SD = 1. 66). As this data does not seem to follow a normal distribution (W = 0. 81652, p = 0. 0012), we used a pairwise one-sided Wilcoxon test to test for differences across groups, which revealed a significant difference (V = 38. 5, p = 0. 032). Hence, we can reject the null hypothesis of independence between conditions and stimulus-elicited pleasure and emotional contagion, but not for cognitive empathy across groups.
We collected subjective data in the form of surveys after each condition which asked questions such as the frequency of chills experienced by the participant, the intensity of the chills experienced, degree to which subjects shared the speaker's viewpoint, and the degree to which subjects shared the speaker's feelings. The subjective questions were asked on a likert scale of 0-10 (see "Supplementary Information" for further details about the procedure, stimulus and questionnaire). Once the experiment was finished, the experimenter disconnected the sensors and provided the subject with a questionnaire. Finally, the subjects were thanked for their participation and fully debriefed. Each session lasted about 20 min. Participants. A total of 21 students participated in the experiment (N females = 7, N males = 14, M age = 27, STD = 6.
5). Sample size was determined based on a compiled exhaustive database of aesthetic chills research (see "Supplementary Information"). We determined that an acceptable sample size for an exploratory study of this kind ranged from 20 to 50. On their arrival at the laboratory waiting room, they were randomly assigned to one of the experimental conditions. Following a within subject design, each participant took part in both experimental conditions: once with and once without the device, each time exposed to the same audiovisual stimulus. The order was counterbalanced to account for learning effects.
An Android mobile application was developed to activate the device with specific timing, delivering the chills in synchrony with the video stimulus. Sensors and software. Physiological data was collected using hand worn sensors from the Dormio device  [57] [58] [59]. We collected heart rate using a Sparkfun Pulse Sensor Amped PPG sensor from the middle finger and electrodermal activity using dry electrodes from the wrist. We also attached a flexion sensor on the index finger and asked participants to clench their index finger when experiencing a chill sensation. The data was sampled at 100 Hz. All the data was collected using Bluetooth to a PC. Facial expressions data was recorded using a camera mounted over the screen and stored in a SD card. We used Affdex SDK for detecting facial expressional from recorded video at 30fps.
Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in "unfamiliar regions".
Introduction At the moment, disinformation has become a real threat in the digital world, also on crowdsourcing platforms. In this respect, Volunteered Geographic Information (VGI) has not been spared as it is also prone to vandalism. The most famous examples of such vandalism include New York City being renamed Jewtropolis in OpenStreetMap (OSM) by a contributor qualified as an "anti-Semite" in the press  [1] , but self-declared as an "ethical hacker" (https://www. reddit. com/r/ openstreetmap/comments/9brqx4/this_is_medwedianpresident1_talking_what_i_did/), or Google workers from India caught reversing one-way streets in OpenStreetMap (OSM)  [2]. Vandalism acts on OSM were estimated to be 0. 2% of changesets in 2018  [3]. In VGI, carto-vandalism designates all mapping contributions that are intentionally perpetrated in order to deface the collaborative spatial database without good reason  [4] [5] [6].
Yet, in order to be reusable and trustworthy, crowdsourced spatial data should ensure a certain level of quality. Therefore, with the aim of providing a minimal level of quality of VGI, one possible solution could be to automatically detect vandalism. Vandalism detection tools have been developed to spot vandalism on Wikidata platforms in an automated way  [7, 8]. However, in the case of VGI, very few tools were devised to address this issue precisely; actually, current research is not able to automatically detect carto-vandalism  [5, 9]. Consequently, this paper investigates the following question: To what extent can vandalism in VGI be automatically detected using supervised machine learning methods? One of the main challenges of supervised machine learning is domain adaptation, also known as transfer learning  [10, 11].
• Which features can we use from OSM in order to build the learning dataset? • Is a classifier, trained on a part of a specific geographic region, able to detect OSM vandalism automatically over the other parts of this specific region? • Can the same classifier detect OSM vandalism in any region of the world? • If we consider a learning dataset, considering several regions, is the model built on it better to detect OSM vandalism in any region of the map? We contribute to this research by first designing a corpus of vandalism on OpenStreetMap (OSM) data, then by experimenting with a supervised learning method, the random forests  [13] , to detect vandalism in OSM. The experimental results' analysis leads us to evaluate the strengths and weaknesses of the approach in order to detect various kinds of vandalism in different regions of interest.
Related Work In order to better understand the mechanisms of vandalism in VGI and OSM, a few studies focused on the past cases of vandalism in OSM. For instance, there were many cases of vandalism caused by players of the Pokemon GO game, and most of them were identified and reverted by experienced OSM users  [9]. In this study, the vandalized features were collected using the comments made by the experienced users when reverting the changes. The study shows that most vandals are young users, as vandalism occurred a few hours after their registration to OSM. In the case of Pokemon GO vandalism, only 16. 5% of vandalized features stayed in the dataset for more than a week. These two results are interesting if we want to detect vandalism automatically. There were also two studies of the list of users banned from OSM  [5, 6].
Even if bans are rarely due to vandalism actions, but more to due misconduct towards the community of OSM contributors, this list gives an idea of the diversity of vandalism cases in OSM. Then, as a significant part of the vandalized features in OSM can remain unchanged for a long time, researchers investigated the automatic detection of this vandalism. Recent research has explored the potential of unsupervised techniques to detect vandalism in OpenStreetMap, in particular by using clustering-based outlier detection algorithms  [5]. However, the output of this approach relies heavily on the characteristics of the dataset. For instance, many buildings in France are mapped by parts. Consequently, OSM datasets from French regions contain small and weirdly shaped buildings (Figure  1 ). Consequently, vandalizing these datasets by mapping small fictional buildings will probably go unnoticed according to an outlier detection algorithm, as the vandalized data are not seen as outliers with respect to the rest of the dataset.
This highlights the main drawback of detecting vandalism using outlier detection algorithms, in the sense that vandalized data that do not fall in the anomaly set cannot be detected. Therefore, the results of this method are not satisfying enough to enable a fully automatic detection of vandalism in OSM data. In  [14] , a supervised learning system, named OSMPatrol, was proposed to detect errors in OSM thanks to a rule-based method. However, this tool not only detects vandalism-i. e. , intentional errors-but also unintentional mistakes. Therefore, exploring the potential of supervised learning methods to detect vandalism in OSM still remains a research agenda. Nevertheless, supervised learning approaches require having a corpus of labelled data available. In this regard, while the corpus of vandalism data exists for knowledge bases such as Wikidata  [15, 16] , to the best of our knowledge, there is no such dataset in VGI.
As for choosing the learning algorithm, the work in  [8]  stated that random forest is the most appropriate method to detect vandalism in Wikidata. Therefore, it seemed coherent to run experiments using random forest, to determine if this method performs as well for vandalism detection in OSM as for vandalism in Wikidata. On another note, recent work has initiated the use of deep learning to assess the quality of VGI data itself  [17]  and even to enhance them  [10]. As detecting vandalism in VGI is a kind of quality assessment, the applications of machine learning to OSM quality assessment are interesting to study.
Pipeline of the Corpus Building So far, detecting vandalism in volunteered geographic information using supervised machine learning has been impossible because there has been no corpus of vandalism data. Therefore, the first step consists of designing such a corpus of data. Besides, a good quality vandalism corpus is crucial to enable the model to properly learn what vandalism is and, therefore, to make it able to detect it. As mapping data may vary from one geographical region to another and as our research question does not focus on detecting vandalism in a specific region, we have to consider different study areas. Let Ω be the set of study areas, i. e. , the set of the different geographic regions in which we will try to detect the possible vandalism. Let OSM x , x ∈ Ω, be the OSM dataset extracted from the corresponding area.
The corpus will contain OSM data that are labelled as vandalism and non-vandalism. As vandalism does not commonly happen in OSM, we assume that the OSM datasets for all areas do not contain a priori vandalized contributions. Therefore, we manually add fake OSM vandalized data to the corpus. For each x ∈ Ω, let V x be the set of vandalized data that are artificially made on x. We denote d x , the database in which OSM x and V x are stored. The general pipeline to build the corpus consists of four steps: the choice of the study areas, the import, the addition of vandalism cases we created, and the inclusion into a database. In the OSM context, each study area will correspond to a specific snapshot. This pipeline is illustrated in Figure  3  for the case where Ω contains four different regions (A, B, C, D).
We also made sure that all four snapshots mainly contained high-quality data in terms of spatial accuracy and completeness. Finally, with visual inspection, we checked that there was no vandalized building in these four snapshots. First, OSM x , x ∈ {A, B, C, D}, are instantiated by downloading OSM data from these regions as historical files from Geofabrik, a free download server. Then, these files are imported into different PostGIS databases d x , x ∈ {A, B, C, D}, thanks to a custom Python script based on the Osmium library. The snapshot date for all four snapshots is 14 February 2018.
Vandalizing the Dataset As most OSM contributors rely on a Bing aerial imagery layer to trace OSM elements, we wanted to mimic this operating mode to produce artificial vandalism. To do this, we used QGIS to display a Bing aerial map base and the OSM vector data layer so that we could see what objects were already mapped in OSM. The production of artificial vandalism (i. e. , instantiating V x , x ∈ {A, B, C, D}) consisted of creating new buildings with the isVandalismfield from Figure  2  set to true or by modifying existing buildings of the OSM building layer (only buildings were vandalised in these snapshots). According to the typology of carto-vandalism proposed by  [4] , we mainly perpetrated play, fantasy, artistic, and industrial carto-vandalism in each study area. In the constructed dataset, play vandalism is represented by large buildings, which may overlap existing OSM buildings.
Main Principles The main idea of this paper is to consider that vandalism can be detected using a classification method, such as random forest, that obtained good results in the case of Wikipedia vandalism. As we created a corpus containing vandalisms in four regions, we considered that we would learn three models from two of them (d A and d B ): a model in each region and a model for their union. We tested these models on the four different areas in order to check: (i) if the model can detect vandalism in its input region; (ii) the ability of the model to be transferable to any region of the map. The third model-i. e. , the model built on the union of the vandalized datasets d A and d B -allows us to study the contribution of variety in the dataset to OSM vandalism detection. The methodology we propose is divided into three steps.
Building a Learning Dataset Data pre-processing is an important step in the experimental protocol because it consists of converting the data into another representation that will be given as the input to the learning algorithm. This new representation is the way the algorithm will see the data. As the configuration of machine learning algorithms may vary from one another, the data pre-processing step should also be adapted to the chosen algorithm. For instance, random forest takes as input a set of features that characterize the data, while a convolutional neural network performs better on a set of images. Therefore, the data preparation step has to be adapted to the chosen machine learning. In our case, this is presented in Section 3. Once the dataset that will be used for the machine learning is built, we denote p x as the processed dataset from d x (Figure  6 ).
Content Features In the case of OSM data, the content of the contribution can be seen at a geometric level and a semantic level. At a geometric level, some features were already proposed in  [5]. They mainly characterize the size and the shape of OSM buildings. Based on these features, we consider the following geometric features: perimeter, edge shortest length, edge median length, elongation, convexity, compactness. These features are used because most of the artistic vandalism cases are made of contributions with unusual geometries. For instance, the top left building in Figure  5  has an extremely large perimeter and an extremely low compactness, while the top right building has a very large perimeter and the edge shortest length. At a semantic level, OSM mapping data have tags, which are key-value pairs that describe the geographic elements.
Context Features As OSM data are geographic elements, they are spatially related to one another  [18]. Besides, as fictional and artistic vandalism consist of mapping elements over existing ones, it is important to consider topological features that quantify the potential overlaps between different types of geographic data that do not usually happen  [19]. We denote building as the geometry of an OSM building object and OSM natural as the set of OSM objects that correspond to natural geographic elements where there should not be any building (water, meadow, forest, etc. ). We define two topological features: n_is_within_lulc = |{nat ∈ OSM natural /building ⊆ nat}| n_inter_lulc = |{nat ∈ OSM natural /building ∩ nat = ∅}| As previously mentioned, context features also include quantifying the contributions' history  [8].
Besides, artificial vandalisms that do not have context features need to be assigned arbitrary values. There was purposely very few examples of vandalized data regarding the number of OSM buildings, as vandalism is quite unusual in OSM (although there is a risk). As the dataset is very unbalanced, there is no way for data augmentation. The solution is to manually generate more vandalized objects in the vandalism corpus. Furthermore, even in the cases where OSM seems to be vandalized, it is complex to be sure it is vandalism and not, only, honest mistakes. The contribution illustrated in Figure  12  appeared in the Aubervilliers area and was not considered as vandalism by our classifiers. There is clearly no building on this esplanade between two large apartment blocks. The name given to this building can be translated to "free esplanade -roof of the parking lot".