dataesr/scientific-paragraphs-categorization huggingface dataset:
). However, this approach raises several challenges. For this kind of consent to be legitimate from a legal point of view, it must be free, specific, informed and unambiguous 69. For example, the categories of data that can be used in P3P or Privacy Bird may be too coarse in many situations and force the users to disclose more data or grant to third parties broader rights than they would really like to. Most languages may also lead to ambiguities or statements that can be interpreted in different ways. Ambiguities may come from the use of vague terms but also from the combination of rules or the default rules  70. One of the criticisms raised against early privacy frameworks such as P3P was their lack of clarity and the divergent (or even misleading) representations of privacy policies  71.
76 https://www. mozilla. org/en-US/dnt. 77 This may not always be obvious for non-technical users though, for example they may not be aware of the fact that different types of cookies may be stored on their computer, some of them directly by their browser, others by Adobe Flash Player, which require different actions. This solution has been used in specific cases such as healthcare information processing 80 but it remains to be seen whether it can become a more widely adopted solution considering the controversies about the trusted computing technology itself, which results in a loss of control of the users on their own computers 81 (and an increased control of the computer manufacturers and software providers).
One solution to redress this imbalance is to provide some form of collaboration between individuals to help them analyze privacy policies and warn their pairs about inacceptable terms. ToS;DR 84  (Terms of Service; Didn't Read) is an example of effort in this direction. The goal of ToS;DR is to create a database of analyses of the fairness of privacy policies and to make this information available in the form of explicit icons (general evaluation plus good and bad points) which can be expanded if needed into more detailed explanations. Users can also install a browser add-on to get the ratings directly when they visit a page. A key aspect of ToS;DR is the fact that users can submit their own analysis for consideration, the goal being that, just like Wikipedia, a group consensus will emerge to provide a reliable assessment of each policy.
For instance, people time and attention are limited. It is impossible to control every single piece of information about oneself which circulates on the networks through myriads of channels and databases. Moreover, while sharing their data with the members of social networks or with various providers, people might suffer from an "illusion of control" 93. Another consequence of the emphasis on active choosing/control is the difficulty raised by the situations where people prefer not to choose. Indeed, the costs imposed on data subjects can be so high in complex and technical areas they are unfamiliar with that majority of them tend to "stick" to default options instead of exercising their freedom of choice and being in control of the situation 94. How can one conciliate the idea of control with the cognitive and behavioral biases that hamper users' privacy and security decision making? On a more analytical level, let's note that even in the hypothetical case where the data subjects would be perfectly aware and competent, the logics of control assumes perhaps too rap-idly that voluntary disclosure of personal information causes no privacy problems  95.
Data Collection Participants completed a baseline set of 5 self-administered, Web-based questionnaires on sociodemographic and lifestyle characteristics, anthropometrics, dietary intake (using repeated 24-hour dietary records), and physical activity along with health status. Thereafter, these baseline questionnaires are administered each year to update the information. All these instruments have been tested against traditional assessment methods (paper questionnaires or interview by a health professional)  [20] [21] [22]. Data on sociodemographic characteristics included age, gender, education, and occupation. In October 2013, participants were sent a computer literacy Web questionnaire in which they were asked to self-evaluate their level of computer skills (novice, inexperienced, experienced, or expert) and to report if they were able to perform specific computer/Internet tasks in order to evaluate their computer literacy profile.
Actual and self-perceived acceptable questionnaire completion times were compared by multivariate linear regression analyses according to the same sociodemographic characteristics and self-evaluated level of computer skills. These variables were log-transformed to improve normality. Adjusted means and standard errors (SE) were reported. Multivariate models were mutually adjusted for gender, age, educational level, and self-evaluated level of computer skills. A P value <. 05 was considered statistically significant. Analyses were carried out with SAS version 9. 3 (SAS Institute Inc, Cary, NC, USA).
Acknowledgments The authors thank  Younes Esseddik ,  Paul Flanzy ,  Mohand Ait Oufella ,  Yasmina Chelghoum , and  Than Duong Van  (computer scientists);  Florence Charpentier  (dietitian);  Elise Martin  (communication assistant);  Nathalie Arnault ,  Véronique Gourlet ,  Fabien Szabo ,  Laurent Bourhis , and  Stephen Besseau  (statisticians); and  Rachida Mehroug  (logistics assistant) for their technical contribution to the  NutriNet-Santé study. This work was supported by the following public institutions:  Ministère de la Santé, Institut de Veille Sanitaire (InVS) ,  Institut National de la Prévention et de l'Education pour la Santé (INPES) ,  Fondation pour la Recherche Médicale (FRM) ,  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Institut National de la Recherche Agronomique (INRA) ,  Conservatoire National des Arts et Métiers  (CNAM) and  Université Paris 13.
At Lighthouse, although all women are exposed to FP messages and made aware of all services during routine visits, only clients who express interest during their ART review are referred to the FP room. For all follow-up visits, women enrolled for FP go directly to the FP area where they also receive their standard HIV care, including ART, therefore reducing visit time. Follow-up FP/ART visits are coordinated to ease client scheduling. VIA services are received only in the FP clinic area as standard ART review rooms are not equipped to perform pelvic examinations. Lastly, in 2013 the national electronic data system (EDS) for HIV services, employed at Lighthouse and MPC, included a new module for provider-initiated screening for risk of unintended pregnancy and active promotion of FP within routine HIV care.
Routine data collection at Lighthouse combines standardised national tools (i. e. FP registers, STI registers) paired with the real-time patientlevel EDS. For evaluation of Lighthouse quality of care, initial patient encounters were observed and each chart was reviewed by an FP-trained physician; concerns were then addressed through one-to-one staff meetings and staff group sessions. Informal FP client discussions were conducted after clinic visits to gauge patient perception and satisfaction. Patient and provider feedback was shared through weekly staff meetings to help identify and create solutions to obstacles encountered, delays in care, and staffing issues. Lastly, as noted previously, in 2013 the EDS incorporated three questions about FP into routine patient management: (1) any current contraceptive use; (2) if yes, type of method(s); and (3) administration of DMPA as promoted in national guidelines.
considered each community separately, instead of the graph as a whole. Second, these observations were made only for two datasets, both representing Social Networking Services (Slashdot and Epinions), so they do not necessarily apply to all networks, or event to all types of networks. In this paper, we want to explore further the informative value of negative links in the context of graph partitioning. To this purpose, we present a method to extract signed networks from voting data describing the activity of the Members of the European Parliament (MEPs). Based on this new data, we apply state-of-the-art tools in order to partition the graph, on the one hand in terms of community structure, and on the other hand according to the notion of structural balance. We then compare the obtained partitions and show the presence of significant differences between them.
The contributions of this paper are essentially two-fold. First, we constitute a new dataset of signed networks and make it publicly available to the community, with the scripts used to obtain it. We treat the voting patterns using several parameters, leading to a collection of signed networks describing the behavior of MEPs according to various modes (time, topic. ). Second, based on these data, we experimentally show that negative links can be essential when partitioning networks. We see our work as complementary to that of Esmailian et al. , first because the use of a method taking negative links into account as a reference allows us to avoid the issue regarding the assessment of intra-community negative links; and second because we treat a different type of signed realworld networks, in which the links represent vote similarity instead of self-declared social relationships.
A. European Parliament Votes In order to be able to conduct our experiments, we were looking for data allowing to extract some form of signed network of interactions. Moreover, in future works, we want to study how the network and the structural balance evolve, so the data had to be longitudinal, with stable nodes (nodes should not change too much through time). The best data we could find relatively to these criteria are those describing the activity of the European Parliament 1. More precisely, we focused on the votes of the Members of the European Parliament (MEPs). The Website VoteWatch Europe 2  is a non-partisan international non-governmental organization, completely independent from national and local governments, from the European Union, as well as from political parties, institutions, agencies, businesses and all other bodies.
In this work we will solve the CC problem using the Parallel ILS algorithm presented in  [6] , which was designed to solve the CC problem in large real-world networks. ILS is itself a metaheuristic approach allowing to obtain good quality solutions by applying iteratively greedy search methods  [46]. Starting from an initial solution estimated through a greedy method, the general principle is two-stepped: first, some perturbations are introduced to modify the current best solution; second, some local searches are performed to find better solutions within the neighborhood. This iterative process is stopped when some condition is met (minimal quality, time limit, etc. ). This specific implementation is parallelized, in order to improve speed. Considering that the networks extracted from the VoteWatch data are very dense, we had to perform some minor modifications on the original Parallel ILS algorithm, so that the processing time was acceptable.
V. RESULTS AND DISCUSSION In this section, we first describe the networks extracted from the VoteWatch data, and how they are affected by the parameters controlling this extraction process. Then, we discuss the results obtained with the partition methods presented in section IV. In order to process the VoteWatch data, we developed a tool called NetVotes, which takes the form of a collection of R scripts. It implements the method described in section III-B, and additionally calculates some metrics describing the studied networks and their partitions. It is generic enough to treat any type of data of the same form. To perform the community detection, we used the igraph R package, which contains all the algorithms we selected. For the CC problem, we used the author's version of Parallel ILS, which we modified as explained in section IV-B.
VI. CONCLUSION In this article, we have investigated some of the aspects inherent to the partition of signed networks, using data from the European Parliament (EP). We first extracted a collection of networks using the voting patterns of the Members of the EP. Then, we applied a selection of community detection methods to these networks, as well as Parallel ILS, an algorithm specifically designed to treat signed graphs. Among the former, the best results in terms of structural balance are obtained, by far, by InfoMap. However, in average, Parallel ILS detected partitions three times more balanced. This seems to be due to the fact community detection methods ignore negative links and focus instead on link density. Independently from the balance aspect, the number of clusters detected by ILS is lower, which is more consistent with the studied system.
These results are in opposition with the finding of Esmailian et al. [14] , however they do not invalidate them. Indeed, in both cases, the experiments were performed on a very limited number of networks. The process should be conducted on a large number of different datasets in order to draw more reliable conclusions. In our future work, we plan to constitute a collection of real-world signed networks in order to perform this task. We also want to continue studying the MEPs voting data in further details, focusing on the interpretation of the identified balanced clusters.
Acknowledgments The present research was supported by the  Swiss National Science Foundation  (grant no. UN8342 ). We wish to thank  Abell Abawary ,  Mathieu Bresciani ,  Clément Kunz ,  Laure Leynaud-Kieffer ,  Maxime Moreillon , and  Charles-Thomas Stern , for their help in data collection. We also wish to thanks the Professors at  EPFL  for having given the permission to hand out the questionnaires during their course.
Monty Hall Dilemma We used an iterated version of the dilemma with 60 trials. A standard interface was used with three doors presented at first. After the initial choice, participants could click on buttons labeled either "stay" or "switch" to make their final choice. They were notified after each decision whether they were successful or not. The next trial came automatically after. In order to ensure a visually presentable MHD task, a completely new version was programmed using Visual Studio 2010 to give it a cleaner interface and smoother controllability. The code used for the task was simulated 60 times with 60 repetitions for every strategy in order to ensure that there was nothing wrong with the programmed structure, i. e. to check if one really did win ≈. 67 times when switching and/or ≈.
Fields surveys Conducted in August 2012, 283 field surveys allowed us to realize a typology of land and build a training zones database (GPS, photo, description of vegetation structure). These training areas were used in the supervised classification model (maximum likelihood algorithm) to map land cover of the 2012 image. A median spatial filter (3 x 3 pixels) was applied to limit the "salt-and-pepper" effect of the classification. Four vegetation classes were mapped on the 2012 image (savannah, young fallow, old fallow and forest). The statistical validation was determined by the confusion matrix and Kappa index  (Oszwald et al. , 2011). The classification of the 2012 image had an overall accuracy of 82% (table  I ) and a Kappa coefficient of 0. 89, which was quite satisfactory. We then used older satellite data to map the years 2006, 2001 and 1984, using the spectral and visual correspondences of the four vegetation types identified in 2012.
, 2015) ; and iii) plantations of acacias and Hymenocardia ulmoides, Markhamia tomentosa, Oncoba welwitschii, Pentaclethra eetveldeana) and a group of 6 secondary species  (Dracaena mannii, Macaranga monendra, Millettia eetveldeana, Millettia laurentii, Sapium cornutum, Vitex congolensis). The height and dbh of 30 trees of each species were measured. The trees were then cut into billons. The length and circumference of each of these wood billons were measured at the large and small ends (minimum 13 cm). Tree volume was calculated using the Smalian equation  (Picard, 2015). Using the Global Wood Density Database  (Zanne et al. , 2009) , the mean wood density obtained was 0. 546 t/ha.
Acknowledgements The authors specially thank all of the  Makala  project workers who were actively involved in inventory and data collection for the preparation of the volume tables, in particular  Simon Diowo ,  Timothée Yamba Yamba ,  Sabu Mbono Wakambo , and  Samuel Lufungula , as well as the  EC  for its financial support (EuropeAid  DCI-ENV 2008-151-384 ). http://makala. cirad. fr.
Data collection and treatment To show how the SCOT created a situation of learning in the ComCom, we used the qualitative method because its purpose is not to measure a phenomenon (which the quantitative method allows) but to give sense to empirical data. Drawing from the study of a single case, the agricultural policy of ComCom, we studied the embedded case  (Yin, 2009)  of the elaboration process of the SCOT. In an abductive fashion, we first started with a preliminary study including the analysis of documents (internal documentations, official documents, previous research papers) and first round of exploratory semi-structured interviews (12 interviews), not only with employees of ComCom, but also farmers and other stakeholders in agriculture on the territory, which allowed us to gain a broad understanding of how the territory functions and to pinpoint the creation of the SCOT as a turning point in the territory's strategy related to agriculture.
, 2009)  and later related actions. This analysis then allowed us to focus on the learning processes behind the events, connecting shifts in instruments, actions and network composition with the interviewees' accounts of the facts. The transcripts of the interviews were then coded according to a pre-established analysis grid to sort the contents of the interviews into themes. Analysis of the qualitative data was done using two methods. First we proceeded to analyse the sequence of events to create templates which allow making visible relationships between certain events  (Dumez, 2013). We then coded the material (transcripts of interviews, documents, and press articles) in a multi-thematic fashion using RQDA 3  (equivalent to Nvivo). These themes came from general theories we used but also from the corpus itself, which allowed us to manage the risk of circularity in the coding process  (Dumez, ibid.
Participants Twenty eight adolescent participants (13 -18 years) were recruited from an Auckland high school as a subsample of participants in the Built Environment and Adolescent New Zealanders (BEANZ) studya crosssectional study exploring the links between the built environment and health in New Zealand adolescents. The BEANZ recruitment procedures are described in detail elsewhere  [33]. Briefly, New Zealand meshblock (smallest census tract unit) walkability indices were calculated for all eligible participants based on their residential addresses, which were obtained from the school's database prior to the consent process. The walkability indices used were consistent with previous research in New Zealand adults  [34]. The subsample was selected from the pool of consenting students, with half of the sample randomly selected from the lowest walkability tertile and the remaining half from the highest tertile in an attempt to achieve variation in environmental exposure  [34].
Data reduction VERITAS-BEANZ questionnaire data (mode of travel, frequency of visits, travel companions) and map data (destination, polyline, and polygon coordinates) were downloaded and imported into ArcGIS 10. 1 (ESRI, Redlands, CA, USA). Perceived neighbourhood boundary coordinates were then converted to polygons using the ET GeoWizards (ET Spatial Techniques, Faerie Glen, Pretoria) point to polygon conversion tool. The shortest network route between the principal residence and each mapped destination was estimated using the Network Analyst Extension and street centreline data obtained from the Land Information New Zealand (LINZ) database (www. linz. govt. nz). Using VERITAS-BEANZ questionnaire data, each of these estimated travel routes were coded as either active travel (i. e. , walking, cycling), passive travel (i.
e. , motorised transport) or mixed travel, which was defined as a combination of both passive and active travel (whether individual trips were multimodal, or different travel modes were used for different trips). All frequency of visits data (reported as either times per week, month, or year) were all converted to times per year for comparative purposes. Using the distance, frequency and mode of travel, a weighted distance metric was computed to estimate the annual distance accumulated by each mode of travel, whilst travelling to each destination. 1 mile Euclidian buffers, 1 km network buffers and corresponding meshblocks were generated for the purpose of comparing the perceived neighbourhood. These buffer distances were chosen because they have commonly been used in adolescent samples  [38] [39] [40] [41] [42].
Measures of urban form Urban form variables have been inconsistently associated with adolescent physical activity in the past  [45] , although the imprecise assessment of the residential neighbourhood may have contributed to these discrepancies  [4]. Three distinct measures of urban form (land use mix, street connectivity, and residential density) were calculated within each of the neighbourhood delimitations. Auckland Council zoning data were used to calculate land use mix. Land was categorised as residential, commercial, industrial, open space, or other. Entropy scores were used to calculate the extent of land use mix using the equation: Land Use Mix ¼ -1 X n i¼1 P i Ã ln P i ð Þ = ln n ð Þ where n is the number of different land use categories and Pi is the proportion of land use category i in the region.
Entropy scores range from 0, which indicates no mix or homogeneous land use, to 1 which represents heterogeneous land use, or a perfect mix. Street connectivity was estimated by calculating the number of intersections with three or more intersecting streets per square kilometre. Intersections were extracted from pedestrian road network data (i. e. , with nonwalkable elements such as highways and on/off ramps removed). All intersections within 10 m were considered one intersection to account for roads that may not align perfectly. As meshblock boundaries are normally defined by street centrelines, a 20 m buffer was applied to each meshblock to include peripheral intersections which may otherwise be omitted. The number of private dwellings per meshblock was obtained from the New Zealand 2006 census  [46].
Analysis Descriptive statistics (mean ± SD) were generated for all data. Normality tests revealed non-normally distributed data so nonparametric analyses were performed. Wilcoxon signed rank tests were used to assess differences between residential neighbourhood delimitations, Mann-Whitney U tests were used to assess differences between genders, and Spearman's correlations were used to test for associations between trip distances, travel modes and travel companions. Significance was set at p <. 05, and all analyses were conducted using IBM SPSS Statistics v22 (IBM Cooperation, USA).
Acknowledgements I would like to thank all the informants referred to in this article for their willful cooperation and useful information. A special thanks goes to my colleagues and friends Liu Pi-chen of the Ethnology Department and  Guo Pei-yi  as well as the other staff members of the  Centre for Asia-Pacific Area Studies of the Academia Sinica  for their hospitality by accepting me as a visiting researcher every year since 2010 and for their precious help in finding my way in the Taiwanese museumscape. Finally, I would like to thank  Louise Tythacott  and  Jim Roberts  as well as two (anonymous) peer reviewers of  Museum and Society  for their constructive comments on previous versions of this article.