dataesr/scientific-paragraphs-categorization huggingface dataset:
Each of these actors controls the resource in a different way. supports is a relation between two Digital Resources (supports(DR 1 , DR 2 )), or a Physical Resource and a Digital Resource (supports(P R, DR)). It means that the target entity could never exist without the source entity. We may say that the latter allows the former to exist. For instance, an operating system is supported by a given hardware, an application is supported by an operating system, or the code of an application supports this application. represents is a relation between Data in the social world and their Instances in the digital world (represents(D,DI)). For instance, the source code of the operating system Windows is a representation in the digital world of the data known as "Microsoft Windows" in the social world.
For sake of simplicity, we consider that a person provides an artifact, if this person owns the data represented by the data instance which supports the artifact. Applying SOCIOPATH makes possible non-trivial deductions about relations among entities. For instance, an actor may be able to access digital resources supported by different physical resources connected to each other (e. g. , a user can access processes running in different hosts). Use case 1 of a SOCIOPATH model: isolated PC. Figure  2  shows a simple SO-CIOPATH model. 5 In the social world, a user John owns some Data and one PC. There are also legal entities as:  We use this example to illustrate some deductions in Section 2. 2. We deliberately propose a trivial example, in order to show clearly how SOCIOPATH can be applied and how some deductions and definitions are drawn.
Use case 2 of a SOCIOPATHmodel: GoogleDocs Figure  4  presents a SOCIOPATH model corresponding to our use case 2 where John uses GoogleDocs for the activity "John reads document. gtxt". In the social world, John owns some Data, a PC and an iPad. We explicitly name only some legal entities who provide resources and artifacts: Microsoft for Windows and Internet Explorer (so called IExplorer), Google for GoogleDocs and Google Cloud services, SkyFireLabs for Sky-Fire, Apple, for the iOS operating system and the browser Safari and Linux Providers for Linux. NeufTelecom, Orange and SFR are telecom companies. John's iPad is connected to SFR Servers and John's PC is connected to NeufTelecom Servers and Orange Servers. In the digital world, the operating systems Windows and Linux are running on John's PC.
Windows supports IExplorer and Linux supports Safari. John's iPad supports the running iOS, which supports two applications, Safari and SkyFire. John's data are represented in the digital world by document. gtxt, which is supported by the physical resources owned by Google. We consider Google Cloud as the storage system used by Google Docs. Analysis and results. Through this example we show that SOCIOPATH provides answers to the motivating questions of this section. Furthermore, by examining the persons who control the artifacts in the paths, cf.
We begin this section introducing how to present a SOCIOPATH model as a directed acyclic graph in Section 3. 1. Section 3. 2 focuses on the main problem that faces trust evaluation that is the existence of dependent paths. We propose to solve this problem by using conditional probability. Section 3. 4, evaluates our contribution with several experiments that analyze the impact of different characteristics of a system on the behavior of the obtained trust values. Experiments realized on both synthetic traces and real datasets validate our approach.
0144 to 0. 9003 as illustrated in Table  5. Thus, collecting the values of trust in each separated node in a system is not enough to determine if the system is trustworthy or not for an activity. One must also know how the system is organized. For example, in α 2 , all the paths contain the nodes A and B and the trust values in these nodes are quite low, 0. 1 and 0. 2 respectively, so the system trust value is also low due to the strong dependency on these two nodes in this system. Influence of the path length and the number of paths on the trust value. This experiment observes the evolution of the trust value for an activity according to some characteristics of the graph like path's length and number of paths. As a dataset, we consider random graphs composed of 20 to 100 nodes, and 1 to 15 paths.
Social evaluation (a real case). In order to evaluate our proposal in a real use case, we modeled part of the SVN system of LINA research laboratory  9  with SOCIOPATH. SVN (Subversion) is a client-server system to manage versions of files. The server allocates repositories of files and clients make copies of repositories. Copies of files contained in repositories can be modified at the client side, modification must be committed to generate new versions. Other clients must frequently update their copies. Persons on which SVN users depend on, are the LINA laboratory that owns the server and the software SVN, the engineer that controls the software at the server side of the SVN, the provider of the software SVN, the computer and the software at the client side, etc. We applied the rules of SOCIOPATH on this system for the activity "a user accesses a file on the SVN".
Due to privacy issues, Figure  12  presents the DAG for this activity with anonymous nodes. For the sake of clarity, we simplify the underlying graph as much as possible. Based on this context, we conducted an opinion survey among twenty members of LINA including, PhD students, professors and technicians about their level of trust in each node. For each person, we have computed the system trust value according  to the methodology presented in Section 3. 3. Table  6  presents the data of the survey and the computed trust values. In a second phase, we asked each user if the SO-CIOTRUSTproposal correctly reflects her trust towards the SVN system used in our lab. The possibilities of answer were simply Yes or No. The last column of Table  6  presents this feedback, where means that they are satisfied, and × means that they are not satisfied.
Demographic surveys offer useful opportunities to collect original data on international migration  (Jensen 2013; Kasnauskienė and Igoševa 2010; Bilsborrow 2007; Zaba 1987). The reconstruction of internal migration trends with survey data is relatively common  (Beauchemin 2011; Piché, Gregory, and Lavoie 1984). However, measuring trends in international migration with survey data is less frequent. The Mexican Migration Project (MMP) was a pioneer in this regard  (Massey 1987; Donato 1998) , focusing on flows between Mexico and the USA. The MAFE (Migration between African and Europe) project 5 also collected data to estimate trends in 3 See  Abel (2013)  for an interesting way of estimating migration flows from tables on stocks. 4 Only legal migrations are recorded in migration statistics. Moreover, data published in some countries refer only to permanent migration (e.
g. , in the US), or they exclude asylum seekers from migration statistics (e. g. , in Belgium). Definitions of migration also vary across countries (e. g. , 3 months in Belgium, 12 months in France). 5 The MAFE project is coordinated by INED (C. Beauchemin)  and is additionally formed by the Université Catholique de Louvain (B. Schoumaker), Maastricht University (V. Mazzucato), the Université Cheikh Anta Diop (P. Sakho), the Université de Kinshasa (J. Mangalu), the University of Ghana (P. Quartey), the Universitat Pompeu Fabra (P. Baizan), the Consejo Superior de Investigaciones Científicas (A. González-Ferrer) , the Forum Internazionale ed Europeo di Ricerche sull'Immigrazione (E. Castagnone), and the University of Sussex (R. Black). The MAFE project received funding from the European Community's Seventh Framework Programme under grant agreement 217206.
The MAFE-Senegal survey was conducted with the financial support of INED, the Agence Nationale de la Recherche (France), the Région Ile de France and the FSP programme "International Migrations, territorial reorganizations and development of the countries of the South". For more details, see http://mafeproject. site. ined. fr/en/. The MAFE datasets are international migration from three African countries (Democratic Republic of Congo, Ghana, and Senegal, see Figure  1 ), as well as trends in return migration. The general approach followed in these projects is to collect a few simple questions on migration in household surveys, and reconstruct trends in migration with event history models. The method consists in reconstructing trends in migration with retrospective information on the first migration (date and destination) collected on all surviving children of household heads.
Advantages and drawbacks of household surveys The general approach to collecting emigration data in household surveys consists in obtaining information on migrants and on a comparison group. Data is collected on people who have migrated (whether they live abroad or have returned) and people who have never migrated. A great advantage of collecting data through a household survey in origin countries is the possibility of obtaining data on migration to all destinations at a relatively low cost 7. Moreover, given that household surveys are conducted regularly in most countries, adding a few questions on migration may be quite cost-effective  (Jensen 2013). The collection of migration data in household surveys also has well-known limitations  (Bilsborrow 2007; Beauchemin 2015). By definition, data on people living abroad are collected from proxy respondents.
When analyzing migration trends over long periods, using household members (at the time of the survey) as the reference group is thus not a valid option. Groups of people defined by permanent links (children or siblings of the respondent) are preferable. In this way, a sample of all the people who previously lived in the origin country can be constituted, and information is collected regardless of their status at the time of the survey (living abroad or not, living in the household or not, alive or not). This is in some ways similar to the data collected on mortality from birth histories or sibling survival histories.
The MAFE data The data used in this paper come from the MAFE project. The MAFE project is a multisite project on international migration. Its objectives and questionnaires were inspired by the Mexican Migration Project  (Beauchemin 2015). The objectives of the MAFE project are to measure trends and patterns of migration, causes of departures and returns, and consequences of international migration on economic and family outcomes. Both household and individual data were collected in 2008 and 2009 in cities of three sub-Saharan countries (Accra and Kumasi in Ghana, Dakar in Senegal, and Kinshasa in DR Congo), and individual data were collected in six destination countries (Belgium, France, Italy, Netherlands, Spain, UK). The same questionnaires were used in all the settings, making data comparable across countries.
Biographic surveys The MAFE biographic surveys are also used in this paper as a means for evaluating some of the assumptions of the household data and for testing alternative data collection methods. Biographic data were collected among individuals aged 25 and over, both in origin and destination countries: non-migrants and return migrants were interviewed in Africa (around 1,500 individuals per selected country), and migrants (at the time of the survey) were interviewed in six European destination countries (between 150 and 280 migrants per destination country: Belgium, Italy, France, Spain, the Netherlands, UK, see Figure  1 ). In Africa, return migrants and partners of migrants were oversampled, and they were between 2 and 4 times more likely to be selected than non-migrants (weights are used to correct for this oversampling).
Life histories were collected, including full migration histories of the interviewees. Interestingly, the questionnaire includes another module that can be used to reconstruct migration trends (even if it was not designed for that purpose): the so-called 'network module'. Each respondent in the biographic survey (regardless of his/her migration status) was asked to reconstruct migration histories of a series of relatives who had lived at least one year out of the country of origin. Figure  3  illustrates the way data was collected in the network module. For each individual (children, brothers, sisters, father, mother, and other relatives or close friends the interviewee could have counted on to migrate), the list of all the changes of country (for at least one year) and the dates of the changes were collected in a grid.
For children, siblings, and parents, the information covers the period from the first departure until the time of the survey: for the others the information is collected from the time of encounter with the interviewee. By selecting the appropriate information registered in the network module it is possible to mimic the data that would have been obtained in the household survey and to thus test some of the assumptions made when computing migratory trends with only the three simple questions. More generally, the network module is useful for evaluating the consistency of migration trends with two different tools, and it also allows documenting joint migration of parents and children. In this paper we mainly use the data collected in the network module in the origin countries. The data were collected from among samples of 1,062 individuals in Senegal, 1,243 in Ghana, and 1,638 in the DR Congo  (Schoumaker and Mezger 2013).
Models As in the MMP  (Donato 1998) , the trends in departure are computed using a discrete time event history model, with only age and period effects (called the age-period model). No other covariate is included, because the purpose of the model is to reconstruct migration trends regardless of the socio-economic characteristics of people 17. Data are organized as a person-period dataset, in which each individual is represented as many times as the number of years between the time s/he turns 18 18 and (1) the first migration, or (2) age 40, or (3) the time of the survey if the person never migrated and is under 40. The migration variable (dependent variable) takes the value 0 for all years, except for the year of migration (last year in the person-period data file) if the individual migrated (value equal to 1).
Temporal frame, sample size and precision In this section we compare estimates obtained with different degrees of temporal detail. Even though the reconstruction of yearly estimates may be desirable for a detailed analysis of migration changes (including the effects of changes in policies or the impact of events like wars and crises), such estimates are hardly reliable. Apart from the fact that data collected from proxy respondents may be inaccurate, reconstructing migration by single year is affected by large sampling errors. Figure  5  shows reconstructed migration trends from the DR Congo between the mid-1970s and the year 2008. As is clear from this figure, confidence intervals are much too large for these estimates to be interpreted in a meaningful way  20. This shows a limitation of the MAFE data -based on relatively small samples -but it raises a more general sampling issue because, in almost all contexts, international migration is a rare event.
Sensitivity of the estimates The MAFE data contains additional information that allows us to evaluate the effects on the estimates of some methodological choices made during the collection or analysis stages. In the first section we use additional information from the household questionnaire to test to what extent the results change if we include (or not) deceased children in the risk set. In the second section we take the baseline results computed with the household data and compare them to the results obtained with the same methodology applied to alternative data (network module from the biographic surveys). And in the third section, we assess what we call the 'filtering effects' of age and destination of first migration (i. e. , the fact that some information is lost when we collect only the information on the first departure).
It is possible to quantify -to some extent -the impact of this assumption with the MAFE data. Data were collected on both surviving children and deceased children of the head of household, and the age at death was collected for the deceased children. Even though data on mortality is not perfect (mortality at young ages is usually underreported), we only use information on mortality above age 18, and we are interested in finding an order of magnitude for the impact that mortality has on migration estimates. Figure  9  shows that the cumulative probabilities of migration do vary when deceased children are taken into account, but differences are much smaller than the confidence intervals. In the three countries, computing outmigration with deceased people included in the data set (until their death) leads to lower estimates.
Using network data to reconstruct trends: a comparison with household data As mentioned before, network data is used to test some of the assumptions of the household survey data. Before doing that we reconstruct first migration trends using the network data and the same method as with the household survey data. We select respondents currently living in the origin countries, and full migration histories of the children of the respondents are obtained from the network module 24. These migration histories are merged with the birth histories of the respondents, so that the dates of birth (and death) of all the children are available. We then select the first migration of each child, to obtain the same information as collected in the household survey. Figure  10  compares trends by broad periods from the two sources.
Filtering effect of age In the household survey, information was collected on the first migration of all the children of the head of the household, regardless of their age at the time of their migration. This may be a sensible approach from a data collection point of view, because any additional criteria may complicate data collection and negatively impact data quality. However, one may be interested in the first migration at an adult age (e. g. , age 18 -as in this paper) and not from birth. Because data is collected on the first migration only, any migration before age 18 will remove the individuals from the risk set for migrations after 18. The baseline results (section 3. 1) were computed in this way. A migration at a lower age will thus have a filtering effect, as it prevents migration at a higher age from being recorded.
Filtering effect of destination Analyzing migrations by destination with the household survey data on the first migration may also be affected by a filtering effect. Since only the date and destination of the first migration was collected in the household survey, any migration to a destination other than the one under study leads to censoring. We discuss this issue by considering migration to Europe. If a person moves from the DR Congo to South Africa in 1996, only that migration will be recorded in the household survey. For the event history models of migration to Europe, the person will be in the person-period dataset until the time s/he moves to South Africa  (1996) , and the dependent variable will be equal to 0 for all the years until the date of censoring. However, the person may come back to DR Congo two years later (1998) and make a move to Europe in 2000.
We used data collected on children of household heads in order to obtain retrospective migration data on a fixed set of individuals, regardless of their place of residence at the time of the survey. We have also shown that migrations of children at adult ages and their parents are not strongly correlated. However, a drawback of this approach is that little information is available for people beyond age 40, especially in earlier periods. Therefore, migration trends can only be computed for ages 18-40 over the last 30-35 years. In low fertility contexts another potential issue is that the number of children would be low and sampling errors greater. An alternative or complementary approach would be to collect data on the respondent's siblings. This would allow better coverage of higher ages and earlier periods; and the number of siblings is also expected to be greater than the number of children.
Not all of the pioneering gold titles survived; not all of the early launched repositories are still operational and online. But in the following years, more and more open access journals were released, supported by initiatives like theScholarly Publishing and Academic Resources Coalition (SPARC) and others, and a growing number of institutions and organizations introduced their own open repository. More than 10,000 journals are indexed in DOAJ; OpenDOAR contains the metadata of about 3,000 repositories. The real figures are higher even if it is impossible to provide a reliable number of all repositories, journals, articles or other items that are part of open access. ArXiv is still there. ArXiv is not only the first and best-known open repository but it is also the best example of the special characteristics of these precursor initiatives: it was not only the right technology at the right time and at the right place, but there was also (and above all) the fact that the HEP community at that time already had a long tradition of sharing resources not on an individual level ("good will") but as a community.
Evaluation and impact: Funding bodies, research organizations and universities have understood the benefits they can get from open access. Free and unrestricted dissemination of scientific information increases the institutional visibility and impact on the Internet counting for international ranking and scientific competition. Also, institutional repositories can be much better tools for academic evaluation and control than library catalogues or bibliographic databases, provided their metadata are more or less exhaustive and linked to current research information systems or functionalities. The institutional need and interest for monitoring and output assessment is a strong driver for green mandatory politics, often supported by academic librarians, less by the scientists themselves. Economic innovation, neoliberalism and evaluation have in common the fact that they are not rooted in the scientific community, that their main goals are not to satisfy scientists'needs but other interests, and that their outcomes paradoxically can satisfy one part of these needs, in particular through more freely available content.
As a consequence, the obtained mechanism adapts the level of noise while achieving the same degree of privacy everywhere. We also show how such an elastic metric can easily incorporate the concept of a "geographic fence" that is commonly employed to protect the highly recurrent locations of a user, such as his home or work. We perform an extensive evaluation of our technique by building an elastic metric for Paris' wide metropolitan area, using semantic information from the OpenStreetMap database. We compare the resulting mechanism against the Planar Laplace mechanism satisfying standard geo-indistinguishability, using two real-world datasets from the Gowalla and Brightkite location-based social networks. The results show that the elastic mechanism adapts well to the semantics of each area, adjusting the noise as we move outside the city center, hence offering better overall privacy.