dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with marine datasets Statistics on differences between the HDGM2016, EMAG2v3 and WDMAM models and marine data are summarized in Table  3. Maps of the most significant differences between HDGM2016 (estimated up to SH degree 720 with 0. 5° grid spacing) and processed filtered marine data or WDMAM (with full grid spacing of 3 arc minutes) and unfiltered data are shown in Fig. 8. Up to SH degree 720 (or through equivalent grid spacing of 0. 5°), differences between global anomaly models and filtered marine data are minimized for HDGM2016 (for all dates) and EMAG2v3. Statistics are relatively similar for both models whether for the NEAWM area or geographical sub-areas. Mean differences for the NEAWM area are estimated at about a few nanoteslas with relatively high standard deviations of about 65 nT.
5° in resolution to about 70 nT for high resolution. By contrast, differences with the 2 arc minutes EMAG2v3 model significantly increase for the mid-Atlantic ridge and English Channel to North Sea sub-areas and consequently for the NEAWM area. Standard deviations for the NEAWM area are estimated at about 90 nT for high resolution compared to about 70 nT at 0. 5° in resolution. Best statistics observed for EMAG2v3 for 0. 5° grid spacing and for WDMAM at full resolution are notable, as both models are globally based on the same datasets and precompiled grids. A noteworthy difference is the use of more realistic synthetic magnetic anomalies generated from ocean spreading  (Lesur et al. 2016)  for WDMAM, in unsurveyed areas. This may partly explain the more favourable statistics for the 3-arc-minute WDMAM model in the mid-Atlantic ridge and the Canary Islands to offshore west Portugal sub-areas.
Conclusions We have presented a comparison of nine global geomagnetic models and their evaluation using an external marine dataset processed according Shom's methodology. Results can be interpreted in the form of a summary diagram that provides a direct overview of the quality of the models (Fig. 9 ). The main findings after comparing global magnetic models are: • Up to SH degree 13: • The WMM2015, EMM2015, HDGM2016, POMME-9, CM4 and IGRF-12 models quite similarly reflect the internal component of the geomagnetic field with relative differences in intensity value of less than 10 nT (for an order of magnitude of 10 5 nT). Differences in inclination and declination values are estimated to be lower than 0. 1° for WMM2015, HDGM2016core and IGRF-12 models. • The COV-OBS. x1 model is interpreted to be equivalent to others but solely for the date 01/01/2010.
HDGM2016 and EMM2015 are thus equivalent overall for both internal and lithospheric components. This result is not surprising because they are built according to the same methods and based on almost the same datasets (see Table  1 ). • The EMM2015 model is doubtful with respect to negative inclination and declination values. For the NEAWM area, the inclination grid is consistent and comparable with the grid derived from HDGM2016. Inclination and declination grids computed from the EMM2010 model are also consistent. EMM inconsistencies for both parameters seem to be observed only for the 2015 version. • Differences between HDGM2016 and EMM2015 comparatively to EMAG2v3 and WDMAM are significant but about one and a half times more for WDMAM (standard deviations of about 110 nT compared to 75 nT for EMAG2v3).
Acknowledgements We would like to thank  Sylvain Lucas  ( Shom ) for his efforts in processing marine magnetic data but also  Corinne Salaün  and  Thierry Schmitt  ( Shom ) for preliminary and final reviews. This work would not have been possible without many scientists and crews (from  Shom ,  Groupe Hydrographique et Océanographique de l' Atlantique (GHOA)  and external international organizations) who collected and made marine magnetic data available. The paper benefited from helpful reviews and comments from two anonymous reviewers.
Availability of data and materials The geomagnetic field models used in this study are publicly available from the following links: IGRF-12 (https ://www. ngdc. noaa. gov/IAGA/vmod/igrf. html), WMM2015 (https ://www. ngdc. noaa. gov/geoma g/WMM/DoDWM M. shtml ), COV-OBS. x1 (http://www. space cente r. dk/files /magne tic-model s/ COV-OBSx1 /), CM4 (http://plane tary-mag. net/earth _inter face. html), POMME-9 (http://www. geoma g. org/model s/pomme 9. html), EMM2015 (https ://www. ngdc. noaa. gov/geoma g/EMM/), EMAG2v3 (https ://www. ngdc. noaa. gov/ geoma g/emag2. html), WDMAM (http://wdmam. org/). The HDGM2016 model is under licence (https ://www. ngdc. noaa. gov/geoma g/HDGM/index. html). External raw marine magnetic datasets are available from SISMER's web portal (https ://www. ifrem er.
Area of interest Model HDGM2016 a 01/01/2010 ANO/720 0. 5°H DGM2016 a 01/01/2015 ANO/720 0. 5°E MAG2v3 a 0. 5°WDMAM a 0. 5°EMAG2v3 b 2′ WDMAM b 3′ NEAWM area (i. e. full dataset) Mid-Atl. ridge 20-45°N Canarian Isl. to offshore west Portugal -1. 50 ± 65. 87 -5. 09 ± 88. 61 -12. 99 ± 62. 96 -5. 40 ± 65. 71 -9. 11 ± 88. 69 -15. 73 ± 62. 65 -5. 75 ± 67. 33 -13. 12 ± 91. 11 -8. 27 ± 118. 65 -8. 74 ± 132. 35 -2. 59 ± 101. 25 1. 01 ± 81. 44 -5. 48 ± 89. 14 -0. 58 ± 69. 41 -13. 34 ± 59. 97 -5. 67 ± 62. 04 -13. 24 ± 55. 94 -7. 66 ± 42. 79 Bay of Biscay English Channel to North Sea W. Mediterranean Sea -0. 50 ± 37. 81 18. 17 ± 36. 05 2. 01 ± 42. 48 -6. 42 ± 37. 87 11. 60 ± 36. 05 -1. 03 ± 42. 39 0. 72 ± 37. 86 8. 65 ± 32. 44 1. 00 ± 49. 22 6. 52 ± 43. 02 9. 52 ± 35. 09 12. 49 ± 50. 51 2. 14 ± 36. 41 2.
Subjects & Image Acquisition We considered 54 healthy volunteers (HV) in this fMRI analysis. All HV had no history of any neurological or psychiatric disease and did not present any contraindications for MRI. For data acquisition, we used a 3T Siemens Trio with body coil excitation and a 12-channel head coil for signal reception. Acquisition of anatomical images was performed using a sagittal three-dimensional T1-weighted Magnetization-Prepared Rapid Acquisition Gradient Echo (MPRAGE) acquisition characterized by a field of view (FOV) = 256 × 256 mm 2 , TR = 2,200 ms, echo time (TE) = 2. 9 ms, flip angle = 10 • , and a voxel size = 1 × 1 × 1 mm 3. The acquisition of rs-fMRI data of the whole brain was performed using a gradient echo echo-planar imaging sequence sensitive to BOLD signal with the following parameter: matrix size = 64 × 64, 45 slices, TR = 2400 ms, TE = 30 ms; flip angle = 90 • , 200 volumes in one session, voxel size = 3 × 3 × 3 mm 3 without gap, acquisition time = 8 min.
LIMITATIONS Identifying of the best strategy for the preprocessing of rs-fMRI data is difficult. This choice depends on the number of subjects, the number of regions constructing the networks and the final objective of the study. In our project we evaluated 12 strategies including smoothing, tCompCor, and filtering with a different order of application and proposed an optimal order of processing steps. Our result may not apply to other data analysis method. Further study is thus needed to extend these results to other datasets with different acquisition parameters and data analysis methods. Controlling the quality of rs-fMRI is a crucial step but remains insufficiently studied. In addition, a similar impact may be expected in pathological subjects.
Browser fingerprinting is a stateless technique, which consists in collecting a wide range of data about a device through browser APIs. Past studies have demonstrated that modern devices present so much diversity that fingerprints can be exploited to identify and track users online. With this work, we want to evaluate if browser fingerprinting is still effective at uniquely identifying a large group of users when analyzing millions of fingerprints over a few months. We analyze 2,067,942 browser fingerprints collected from one of the top 15 French websites. The observations made on this novel dataset shed a new light on the ever-growing browser fingerprinting domain. The key insight is that the percentage of unique fingerprints in this dataset is much lower than what was reported in the past: only 33.
INTRODUCTION Web browsers share device-specific information with servers to improve online user experience. When a web browser requests a webpage from a server, by knowing the platform or the screen resolution, the server can adapt its response to take full advantage of the capabilities of each device. In 2010, through the data collected by the Panopticlick website, Eckersley showed that this information is so diverse and stable that it can be used to build what is called a browser fingerprint to track users online  [15]. By collecting information from HTTP headers, JavaScript and installed plugins, he was able to uniquely identify most of the browsers. With the gathered data, Eckersley not only showed that there exists an incredible diversity of devices around the world but he highlighted that this very same diversity could be used as an identification mechanism on the web.
While Panopticlick and AmIUnique proved that tracking is possible, one problem arises when looking at both datasets: their bias. First, both websites are dedicated to fingerprinting, people who visited them are interested in the topic of online tracking. It limits the scope of their studies. Then, looking at the general statistics page of the AmIUnique website from July 2017, we can clearly see a bias as 57% of visitors are on Windows, 15% on Linux, 13% on Mac, 5% on Android and 4% on iOS. The latest statistics from StatCounter for the month of July 2017 reveal that the OS market share is dominated by Android with a percentage around 40%, followed by Windows at 36%, iOS at 13%, Mac at 5% and Linux under 1%  [6]. One can then ponder about the impact of such a big difference on the effectiveness of browser fingerprinting.
In this paper, we investigate whether tracking can be extended to websites that target a broad audience. We analyze 2,067,942 fingerprints collected from one of the top 15 French websites, and we investigate whether browser fingerprinting techniques are still effective in identifying users by collecting the same attributes reported in the literature. Our first two research questions are related to this issue: RQ 1. How uniquely identifiable are the fingerprints in our data? RQ 2. Can non-unique fingerprints become unique if some value changes? The other questions are related to the characteristics of the dataset and the possible impact of the evolution of web technologies: RQ 3. Can the circumstances under which fingerprints are collected affect the obtained results? RQ 4. Does the evolution of web technologies limit the effectiveness of browser fingerprinting? Where previous studies reported having above 80% of unique fingerprints, we obtained a surprising number: 33.
We also provide the first large-scale study of JavaScript font probing and we measure its real-life effectiveness. • We show that by collecting these attributes and targeting a much broader audience, browser fingerprinting is not as effective as it was reported in the literature. While previous studies reported having above 80% of unique fingerprints, we obtained 33. 6%. • We compare our dataset with the ones from Panopticlick and AmIUnique and we explain in details the numerous differences that can be observed. • We provide a discussion on the future of browser fingerprinting and what these results mean for the domain and for future applications of this technique. The paper is organized as follows. Section 2 introduces our new dataset along with the ones from Panopticlick and AmIUnique. Section 3 analyzes the diversity of browser fingerprints in our data and compares the three datasets by providing detailed statistics to help explain the differences.
Previous studies 2. 1. 1 Panopticlick. In 2010, Peter Eckersley launched the Panopticlick website with the goal of collecting device-specific information via a script that runs in the browser  [15]. The script collected values for 10 different web browser features and its execution platform. Features were collected from three different sources: HTTP protocol, JavaScript and Flash API. Eckersley collected 470,161 fingerprints from January 27th to February 15th, 2010. Data obtained by Panopticlick is "representative of the population of Internet users who pay enough attention to privacy"  [15] , so in this sense the data is quite biased. In the study performed by Eckersley, the list of fonts (collected through the Flash API) and the list of plugins (collected via JavaScript) were the most distinguishable attributes.
The dataset The fingerprints used in this study have been collected through a script deployed in collaboration with the b<>com Institute of Research and Technology (IRT) on one of the top 15 French websites (according to the Alexa traffic rank) on two specific web pages: a weather forecast page and a political news page. The script ran for a six month period, from December 7th, 2016 to June 7th, 2017. To be compliant with the European directives 2002/58/CE and 2009/136/CE, and with the French data protection authority (CNIL), only visitors who consented to the use of cookies, and thus the use of fingerprinting techniques, were fingerprinted. When users first connect to one of these two pages, we set up a 6-months long cookie in their browser. This supports the identification of returning visitors.
Compared to the other two detailed studies, the website used to collect this dataset covers a wide range of topics and it is not dedicated to browser fingerprinting. According to the Hawthorne effect  [23] , if individuals are aware that they are being studied, a type of reaction occurs, in which individuals modify an aspect of their behavior in response to their awareness of being observed. In our case, this means that the fingerprints in this dataset are more representative of those found in the wild, since users are not enticed to play with their browsers to change their configuration and produce different fingerprints.
This means that fingerprinting scripts must turn to another mechanism to get access to the list of fonts. Nikiforakis et al. revealed that it is possible to probe for the existence of fonts through JavaScript  [29]. A script can ask to render a string with a specific font in a div element. If the font is present on the device, the browser will use it. If not, the browser will use what is called a fallback font. By measuring the dimensions of the div element, one can know if the demanded font is used or if the fallback font took its place. The biggest difference between these two gathering methods is that fonts through JavaScript must be checked individually whereas Flash gives all the installed fonts in a single instruction. This means that testing a large number of fonts is time consuming and can delay the loading of a web page.
For this reason, we chose to test 66 different fonts, some among the most popular 'web-safe fonts' which are found in most operating systems and other less common ones. Appendix A reports on the complete list of fonts we tested in our script. Before deploying our script in production, we identified a limitation in how JavaScript font probing operates. We found out that some fonts can have the exact same dimensions as the ones from the fallback font. Figure  1  illustrates this problem. In the example, the two tested fonts are metrically comparable and have the exact same width and height. However, they are not identical as it can be seen in the shapes of some of the letters (especially "e", "a" and "w"). This means that font probing here will report incorrect results if one were to ask Times New Roman on a system with the Tinos font installed (or vice versa).
In order to see how far we can go with this technique, we took as a basis the canvas test performed by Laperdrix et al. [22]  and we made a more complex canvas element by combining new elements of different natures. First, the script asks the browser to render the two following strings: "Yxskaftbud, ge vår WC-zonmö IQ-hjälp" and "Gud hjälpe Zorns mö qvickt få byxa". Both strings are pangrams (a string with all the letters of the alphabet) of the Swedish alphabet. For the first string, we force the browser to use one of its fallback fonts by asking for a font with a fake name. Depending on the OS and the fonts installed on the device, fallback fonts may differ from one user to another. For the second line, the browser is asked to use the Arial font that is common in many operating systems. Then, we ask for additional strings with symbols and emojis.
All strings, with the addition of a rectangle are drawn with a specific rotation. A second set of elements is rendered with four mathematical functions: a sine, a cosine and two linear functions. These functions are plotted on a specific interval and using the PI value of the JavaScript Math library as a parameter. The third set of elements consists in drawing a set of ellipsis. These figures are drawn with different colors and with different levels of transparency. Since filters for opacity change among browsers, it creates differences between them. The last element is a centered shadow that overlaps the canvas element. Figure  2  displays an example of a canvas rendering following the instructions of our script. Cookies. Since we only have fingerprints from users who accepted the use of cookies, all fingerprints have the exact same value for this attribute.
Descriptive statistics. We distinguish two different kinds of fingerprints: those belonging to mobile devices and those belonging to desktop and laptop machines (we will refer to desktop and laptop machines as personal computers). To prevent collecting multiple copies of the same fingerprint from the same user, we store a cookie on the user's device with a unique ID for six months. Among the 2,067,942 fingerprints, the distinction is as follows: 1,816,764 come from personal computers (87. 9% of the data), and the rest, 251,190 fingerprints come from mobile devices (12. 1% of the data). 1  reports on the distribution of operating systems in both our dataset and the one from the AmIUnique website. Statistics gathered from StatCounter for the month of July 2017 have also been added to give an idea how close they are from the global population.
First, by looking at the differences between our newly collected data and AmIUnique, we can see that there is a significant difference in terms of distribution. Notably, we can see a clear bias in the demographic that AmIUnique attracted since the percentage of Linux desktop machines is much higher than the reported by StatCounter. Then, if we compare our numbers with the ones from StatCounter, we can see that we provide a closer representation of the global population as the percentages for both distributions are close to each other. Table  2  summarizes the essential descriptive statistics of our dataset. The 'Distinct values' column provides the number of different values that we observed for each attribute, while the 'Unique values' column provides the number of values that occurred a single time in our dataset.
Browser fingerprint diversity Our data was collected on a much larger scale than previous studies and targeting a much broader audience, which leads to the RQ 1. How uniquely identifiable are fingerprints in our data? This question aims at determining how diverse the browser fingerprints are in this novel dataset. Using attributes from Table  2 , we succeeded in uniquely identifying 33. 6% of fingerprints in our dataset. On personal computers, 35. 7% of fingerprints are unique while this number is lower on mobile devices with 18. 5%. On personal computers, the threat is less important than reported in other studies. On mobile devices, the number is much smaller but the threat comes from elsewhere: closed platforms with integrated tracking applications. Figure  3  represents the distribution of the anonymity sets.
Unique fingerprints. There are 46,459 unique fingerprints on mobile devices and 647,741 on personal computers. A fingerprint is unique due to one of the following reasons: • It has an attribute whose value is only present once in the whole dataset. • The combination of all its attributes is unique in the whole dataset. On mobile devices, 73 % of fingerprints are unique because they contain a unique value, while this percentage is around 35% for personal computers. While mobile fingerprints tend to be unique because of their unique values, laptop/desktop fingerprints tend to have combinations of values so diverse that they create unique fingerprints. The most distinctive attributes are canvas on mobile devices and plugins on personal computers. Fingerprints with unique canvas values represent 62% of unique fingerprints on mobile devices, while on personal computers, fingerprints with unique combinations of plugins represent 30% of unique fingerprints.
Users are allowed to change these values at anytime. Besides, some attributes such as timezone or fonts are indirectly impacted by a Let us take as an example of a user with a non-unique fingerprint. Running Chrome 55 on Windows 10, the browser displays the following value for the Content language header: fr-FR,fr;q=0. 8,en-US;q=0. 6,en;q=0. 4 For some reason, the user decides to add the Spanish language. The browser then displays the following value: fr-FR,fr;q=0. 8,en-US;q=0. 6,en;q=0. 4,es;q=0. 2 By changing the language settings, does the fingerprint become unique? In order to answer this type of question and to study how resilient non-unique fingerprints are in the face of evolution, we conducted an experiment. We looked at analyzing the impact made by the user's choice on the uniqueness of their fingerprints.
There is a set of attributes whose values cannot be changed such as attributes related to the hardware and software environment on which the browser is running. The Platform attribute is linked to the operating system, while WebGLVendor and WebGLRenderer reveal information about the GPU. Attributes such as User-agent, List of HTTP headers or Content encoding are beyond the control of the user because they are related to the HTTP protocol. However, attributes such as Cookies enabled, Do Not Track, Content language and List of plugins are a direct reflection of the user's choice. Nevertheless, Cookies enabled, Do Not Track, Use of local/session storage are limited to "yes" and "no", so they do not offer a very discriminant information. This leaves the Content language, List of plugins, Available fonts and Timezone under the scope of our analysis.
For the experiment, we chose fingerprints belonging to sets larger than 50 fingerprints. New values were chosen randomly from nonunique fingerprints that had the same operating system and web browser (including versions). This was made to ensure that the new values are consistent with fingerprints that can be found in the wild. This way, we avoid choosing values that are not characteristic of the fingerprint environment. For example, two browsers can have the same language configuration, but the encoding is different depending on the web browser. Example: Windows 8. 1, Chrome, fr-FR,fr;q=0. 8,en-US;q=0. 6,en;q=0. 4 Windows 8. 1, Firefox, fr-FR,fr;q=0. 8,en-US;q=0. 5,en;q=0. 3 Both browsers are running on the same operating system and have the same language configuration: French/France[fr-FR], French[fr], English/United States[en-US] and English  [en].
Comparison of attributes Mathematical treatment. We used entropy to quantify the level of identifying information in a fingerprint. The higher the entropy is, the more unique and identifiable a fingerprint will be. Let H be the entropy, X a discrete random variable with possible values x 1 ;. ; x n and P(X ) a probability mass function. The entropy follows this equation: H (X ) = - n i=0 P(x i )loд b P(x i ) (1) We use the entropy of Shannon where b = 2 and the result is expressed in bits. One bit of entropy reduces by half the probability of an event occurring. In order to compare all three datasets which are of different sizes, we applied the Normalized Shannon's entropy: H (X ) H M (2) H M represents the worst case scenario where the entropy is maximum and all values of an attribute are unique (H M = loд 2 (N ) with N being the number of fingerprints in our dataset).