dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods comparison using the UK BiLEVE dataset For the results presented in Figure  1  of the main paper that compares methods each phasing run was performed on independent Amazon EC2 m2. 2xlarge instances to avoid any possibility of diminished performance from other running processes. The elapsed time of each run was measured with the GNU time command. HAPI-UR results were generated with v1. 01 of the software, using a window size of 80 as suggested in the HAPI-UR manual for a microarray of this density. SHAPEIT2 results were generated with version r778 of the software using the default parameters. Computation time (left) and switch error rate (right) of different phasing routines. Estimated haplotypes are compared to those derived from the IBD1 segments of the 384 likely sibling pairs. HAPI-UR 1X is the average switch error rate/time across three runs of HAPI-UR.
the performance on a subset of the full dataset. The clustering column indicates whether the new method for choosing copying states was used or not. The new MCMC column indicates whether the new MCMC routine, which uses completely parallel updates and local algorithm termination, was used or not. Performance is measured as median switch error on the trio children. Run time is given in hours. The Scaling column shows the relative run time compared to the SHAPEIT3 run on a sample size of 1,072. 10 threads were used for all runs.
Acknowledgements UK BiLEVE was funded by a  Medical Research Council (MRC)  strategic award to MDT, IPH, DS and LVW (MC_PC_12010). We would like to thank the  UK BiLEVE study investigators , array design team, analysis group and steering group. This research has been conducted using the  UK Biobank Resource. This article presents independent research funded partially by the  National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the Department of Health. J. M acknowledges support from the  ERC  (Grant no. 617306 ).
With the adoption of Open Data Policy, many government data sets are now freely available to the public. The latter includes data from some European National Mapping Agencies (NMAs). For example the NMAs of Finland, The Netherlands, Germany, Great Britain and France have released at least a part of their data for free access. In most cases these data were integrated in OpenStreetMap (OSM). This has resulted in enhancements to the OSM data set but, critically, may have benefits for the NMAs. For example, Begin  [5]  describes how the Canadian NMA released data to OSM and as a consequence data updates could be received from OSM contributors instead of NMA surveyors. The potential role of citizen sensing in the work of bodies such as the NMAs has been identified  [6, 7]  but has not focused on the actual and potential use of citizen sensor data in the mapping practices of official government agencies.
The vast potential of citizen sensing in mapping  [8]  is, however, limited by concerns especially in relation to data quality  [9, 10]  and the trustworthiness of the data sets generated as compared with those from authoritative bodies such as the NMAs. However, authoritative bodies often struggle to acquire data at appropriate spatial and temporal dimensions and could potentially benefit from data provided by citizen sensors  [11]. This article explores the current and potential role of citizen sensing in the work of authoritative European regional and national mapping, drawing on the results of a survey conducted from December 2013 to February 2014 and two workshops organized by the French Mapping Agency (IGN France) in March 2014 and by Swisstopo in February 2015. The work was conducted as part of COST Action TD1202 "Mapping and the Citizen Sensor"  [12].
Specifically, 11 mapping agencies adopted a cyclical policy, six a continuous policy and two used both cyclical and continuous updating, although their primary revision policy was continuous. Unexpectedly, the survey showed that four of the organizations had not yet established policies, reflecting that they are relatively new. In the context of using VGI it is anticipated that volunteers might be particularly able to contribute information on change that is most suited to continuous revision. Volunteers might, for example, highlight areas that a mapping agency might wish to send professional survey staff to visit or submit new data that could be integrated into existing databases. It would still be possible for volunteers to contribute to cyclical revision but this might be best achieved in a highly steered activity with, for example, volunteers encouraged to focus on particular areas at a particular time.
It is also apparent that in most cases current involvement is limited to change detection and error correction. Only two agencies use VGI as a primary data source or to collect data they would not otherwise collect. This situation suggests that mapping agencies do not currently fully exploit the new opportunities offered by VGI perhaps because of various concerns, some of which are discussed further below. Nine NMAs have plans either to start collecting VGI (six agencies) or to expand further what they currently collect (three agencies). Those that did not have firm plans had a range of concerns that included worries about: infringement of copyright of data when using third-party VGI such as OSM, data quality, and data reliability. The remainder of this section focuses on some specific examples issued from workshops of how NMAs have engaged with VGI and their concerns with the use of VGI.
Examples of VGI Use While open source tools are available such as OSM tools, it was evident that most of the mapping agencies surveyed had developed their own tools to collect data from the crowd or public partners. It should be noted that the maturity of tools varied from those developed for research projects to well-proven, fully operationally tools. This section gives an overview of some of the tools that have been developed and examples of the use of VGI in relation to the topics defined in Figure  5.
Report Alerts IGN France developed a report alert system called Ripart, which has been in service since 2008  [23]. It is a communitysourcing system where professional partners, such as the fire service, propose updates to IGN data by completing a form and providing location information using GPS tracks, photographs or drawings on a map. Ripart's reporting process provides overlapping sketches of the proposed changes on an inert base map. Thus, the contributing partners do not have the ability to act directly on features belonging to the authoritative IGN database; to overcome this inconvenience a new version of the tool is being tested that allows partners access to a copy of topographic database (the last update: Day 1) and are able to directly add or modify geographic features. The National Land Survey of Finland, which made its topographic database open to the public, has developed both a map interface (http://www.
karttapaikka. fi) and a mobile application (http://www. karttaselain. fi) to collect feedback from citizens. Annually, it is estimated that some 500-700 reports are made using the map interface as well as 200-300 from the mobile application. The messages received are checked, replied to and most generate modification(s) in the agency's topographic database. Some updates are checked in the field or await to be verified and captured from scheduled aerial imaging. In The Netherlands, Kadaster has developed a feedback system, Topographical Key Register (BRT) to collect feedback from map users. The system is deployed on tablets, smartphones and web platforms and is based on ArcGIS solutions. The data collected are validated after quality analysis and integrated in the database by surveyors from Kadaster.
The agency of Switzerland, swisstopo, has a similar system in operation, which is integrated in the Web infrastructure of the Federal Spatial Data Infrastructure (FSDI). The so-called revision client allows feedback from citizens from all standard platforms. The feedback from the citizens is validated and directly used in the map update process by the operators. IGN Spain developed a report tool based on the National Geographic Information System (SIGNA) Geoportal. An online earthquake survey has also been developed that allows citizens to report earthquakes having at least 2. 5 Richter scale magnitude or those that were just felt by the population. The Ordnance Survey of Great Britain has traditionally taken error and change reports from citizens via landline telephone calls and letters. A web-based tool has been successfully trialed with public sector customers for reporting errors and omissions in a range of Ordnance Survey products.
Change Detection Change detection is an important activity for most mapping agencies and can benefit from VGI. In The Netherlands, Kadaster undertook a pilot test for change detection relating to roads and buildings in its 1:10k dataset (TOP10NL) from external sources (e. g. local administration datasets and OSM). This found that changes could be detected much more quickly than with the methods used conventionally at Kadaster. At IGN France, change detection is made by land surveyors that analyse a range of alert types and contact local administrators. It estimates that 75% of working time was dedicated to change detection, leaving only 25% for field work. It plans to develop a system to automatically infer change from the sources used by the surveyors, and from additional sources found on the web, including VGI from OSM, GPS tracks from hiking websites or Wikipedia.
New Data Collection VGI provides potential to capture new content, data on features not previously collected by the mapping agencies as not within their mission priorities or excluded for political or economic reasons. The Federal Agency for Cartography and Geodesy of Germany uses VGI to collect new content, features or attributes, such as buildings, structures (e. g. bridges, pylons, silos, dams), roads, tracks and paths, hydrographic features (e. g. rivers, lakes, canals), names, points of interest (e. g. tourist attractions, cash machines). New data are collected via secondary sources such as OSM. The NLS of Finland also found that cadastral boundary information could be acquired from owners with a high level of precision, typically within 0. 5-4. 0 m of reality. In spring 2015 IGN France launched a crowdsourcing application dedicated to outdoor activities (http:// espaceloisirs.
There has been little research on the perceived lack of homogeneity of VGI (e. g. a lack of overall consistency in terms of what is mapped, how detailed the mapping is, and how it is then classified) and its impacts; but some recent research on OSM showed that the scale or level of detail of individual features can be inferred. For example, in some VGI data sets the information on buildings in some places may be very detailed because it has been imported from available cadastral data, but less detailed elsewhere as, perhaps, captured from satellite imagery. It has been shown that key details of individual features may be inferred from their geometry, semantics or metadata  [16, 17, 18]  allowing the variation to be quantified. This then offers the potential to allow it to be integrated into NMA's mapping processes that are made for homogeneous data, as the variability of detail can be clearly differentiated.
Legal Issues Integrating VGI data in authoritative mapping agency databases also raises legal concerns, including those connected with intellectual property rights, privacy, and legal liability  [30]. VGI hosts generally protect their intellectual property interests in their websites by copyright laws. Since authoritative databases also have copyright laws, although the scope of protection may vary significantly, it becomes unclear who owns and is responsible for the data if different sources of data are integrated or used. Research on copyright and public domains should help the mapping agencies address some of the important legal concerns that they have  [31, 32]. As an example of licencing issues, IGN France and OpenStreetMap France are collaborating on a joint project to build an open dataset for the crowdsourced collection of unified addresses.
One of the main technical problems is the incompatibility between OSM's ODBL licence and the French administration's open licence, which has forced the duplication of data on several servers. Legal liability is an important issue for national mapping agencies since errors in the data due to the integration of imperfect VGI data could cause physical injuries or even the loss of life. In such cases, the issue of who is liable for the incorrect decision-making and consequential damages arises. This issue is linked closely to the concerns with data quality noted above. Scassa  [32]  argues that legal liability of VGI would encourage contributors to produce quality data but discourage non-confident contributors. The creation of associations of volunteers can also help volunteers to avoid legal liability risks  [33].
A key attraction of VGI is that the contributor often has in-depth local knowledge  [1]. The data provided may not be homogenous but with some themes such as roads can be of better quality than authoritative data sets, certainly in terms of completeness and required positional accuracy, at least for urban areas  [9]. The desire to exploit this information and engage a large community has driven research on the motivation of the volunteers. People are motivated in a variety of ways. They may be motivated emotionally, as in a natural disaster  [45]  or when the action is in their own best interest. Cadastral mapping is an example where contributors (generally owners) are motivated to map and update the cadastre helping to reduce costs and time as well as removing errors  [46, 47]. Maintaining the interest of contributors was seen as important and this benefited from the provision of feedback to contributors and the rapid update of the topographic database.
There is a risk here that VGI contributors may possibly be motivated by building an alternative to authoritative data (a kind of libertarianism) and may not be attracted to NMA's VGI systems. More specifically, it is quite unlikely that capturing authoritative VGI will attract all OSM contributors, so the NMAs will have to motivate different contributors to build a community large enough to support their needs. This is an issue that should be studied in future work. Mapping agencies from Finland, France and Netherlands mentioned that people feel rewarded when they notice that their contribution is taken into account. For example, Netherlands' Kadaster has good experience in collecting VGI data by providing a prototype system comprising web, mobile phone and tablet applications; emails, tweets and an interactive human computer interface are used to inform users who reported changes or errors.
The survey and workshops that underpin this article sought to better understand the practices, experiences and needs of national mapping agencies concerning VGI, especially as a means to share information and support a wider engagement with the VGI community. It was evident that most of the mapping agencies involved had some engagement with the VGI community and others have plans to develop this. Opportunities for the use of VGI were apparent, especially given the positional accuracy of some VGI data sets and the potential for use with respect to continuous revision. Indeed, in agencies using citizen sensor data, VGI was often used for change detection and the identification of errors. Only very rarely, however, was it integrated directly into the authoritative databases. The activities of communities such as those linked to OSM has not changed the way mapping agencies produce data and the majority did not view OSM data as currently being highly useful.
Indeed, for most NMAs, the integration of VGI data directly in authoritative databases was not viable. The inexpensive nature of VGI was not an issue to the NMAs. It was evident that for countries where the data coverage was incomplete VGI was not being used, nor planned for use, to complete it. This happens because NMAs are state entities that are officially responsible for providing accurate and reliable spatial data infrastructure (SDI) to all potential users; hence, they are wary of potentially flawed VGI provided by the public. Although VGI offers the mapping agencies potentially useful data, a series of obstacles to its use were evident, notably relating to issues such as data quality, legal issues and sustainability. Some research on these has been undertaken but further research needs to be conducted to enhance the ties between mapping agencies and VGI community.
Much of the research on issues of data quality has so far focused on issues such as positional accuracy and to a lesser extent completeness and classification accuracy; much more research is needed into issues such as updatedness, maintenance or currency and legal issues. Priorities need to be established to help define strategies that can make use of VGI. Many of the mapping agencies are developing tools to collect data from public partners or the crowd and hence activity to identify good/best practices will be helpful. How can mapping agencies create dedicated communities that persist over time? Should there be active collaboration with groups such as those contributing to OSM? How will mapping agencies use VGI while respecting INSPIRE rules? The INSPIRE directive aims to create a European Spatial Data Infrastructure.
Acknowledgements This study was supported by the  COST Action TD1202 Mapping  and the  Citizen Sensor  funded by the  European Union Seventh Framework Programme  and the  European Science Foundation. The authors would like to thank the  COST Action   TD1202 , the national mapping agencies which responded to the survey and the representativesof NMA who participated in the meeting:  Peter Penev ,  Andreas Hadjiraftis ,  Marie Laasko ,  Loic Gondol ,  Bianka Fohgrub ,  Magdalena Grus ,  Jusper Hogerwerf ,  Oana Popescu ,  Ana Velasco-Tirado ,  Pär Hedén  and  Chris Philips. Finally, we are grateful to the four referees for their helpful comments on the original manuscript.
• Simulate RFID cyber-physical infrastructure in unknown indoor environment that may contain operators, robots, places, obstacles, known and unknown objects using Player/Stage simulator. • Simulate RFID readers of the Robots agents with fiducial finders, RFID tags with fiducials attached to all the environment elements so they can be read by these sensors. • Organize all data collected by the Robots agents into a centralized database by the Supervisory agent. This database is accessible by the MRS. Besides this introduction, this article is organized into four sections. Section two presents RFID technology, its main advantages and drawbacks. Section three describes in details our proposed RFID CPRS control architecture, and presents the proposed solution for tags detection and identification.
II. RADIO FREQUENCY IDENTIFICATION In general, RFID technology is widely used in CPRS. It consists of an automatic identification technology and data capture that allows different entities to be uniquely identified. In recent years, it is possible to equip almost all objects of the environment with small RFID tags at very low cost. These improvements make this technology very attractive to be incorporated in robotics field. Indeed, these tags and sensors provide rich information and data that can be very useful to robots, to help them carrying out their various tasks (navigation, mapping, people tracking, etc. ) or used as a support for accurately localize robots inside the environment, detect obstacles and eventually know their nature (fixed/mobile, doors, objects, etc. ), robots (mobiles/manipulators/mobile manipulators), etc.
Advances in manufacturing have produced miniaturized radio transponders called RFID tags that can be attached or integrated into very small objects  [7]. Each tag has a unique identifier and small-size memory (64-2048 bits) to allow storing data  [8]. RFID technology allow to identify an object, to follow its path, to know its characteristics and to quickly have accurate, reliable and consistent information through a tag, that emits radio waves, attached or incorporated into objects. Suitable devices, called RFID readers, can access RFID tags by radio, either for read or write operations. Tags capture energy and transfer stored data/store data accordingly using the power scavenged from the signal coming from the RFID reader  [9]. As depicted in Fig. 1 , RFID system consists of the following components  [7] : • RFID tag (Transponder): it is an electronic identification device which consists of a chip with an antenna.
• RFID reader (Base station): it aims to identify the transponder. Recent RFID readers are divided into shortrange and long-range readers depending on the distance within they can access RFID tags. Such a distance may vary from few centimeters up to several meters. • Data processing system (Computer system): it consists of a computing infrastructure used to collect and exploit data. • RFID tags can be re-written with new information and as such are re-usable and updatable with new information about the object to which they are attached to. • RFID tag has a much greater data capacity; it can store large amounts of data additionally to a unique identifier. • Long-life service; tags are less sensitive to adverse conditions (dust, chemicals, physical damage, etc. ). • High writing and reading speed.
B. RFID technology drawbacks On the other hand, RFID technology has some drawbacks  [10]  [11]: • RFID technology proves to be unreliable in mediums like metals or liquids where RFID tags fail to be read. • Significant percentage of RFID tags fails to function properly (about 20-30% of tags). • RFID tags can suffer from orientation problems as sometimes these tags do not interact with the readers when both are misaligned with respect to each other. • RFID technology is expensive even though prices are decreasing. For the use of RFID tags, a particular adopter has to first install RFID reader(s) and computer networks for assessing information in the RFID tags. This leads to an expensive installation cost. • Depending on the field of application, it may become necessary to prevent unauthorized persons from reading or writing data stored on or transmitted from tags.
Supervisory agent This hybrid agent is responsible of the coordination and synchronization between the entire Robots agents. The whole process starts by receiving, from the human operator, the request to detect and identify the tagged objects of the environment (REQUEST message) within the number of the robots team (RMax). After that, the Supervisory agent creates the required number of mobile robots, sends them an INITIALIZE message and waits for their responses. When the Supervisory agent receives the INITIALIZED messages from all the active mobile robots team, it replies by sending them the DETECT message. Just after receiving this new message, the Robots agents start moving inside their environment. Thereafter, each time the Supervisory agent receives a DETECTED message within the ID of the detected tagged object, its current coordinates and the detection time, it looks-up for the tag ID into the centralized database and gets all the object information.
B. Simulation of the ground floor of the DPR We have simulated a population of mobile robots in the ground floor of the DPR of the CDTA research Center. This environment, of about 32x22 meters square, is described in Fig. 6. All the operators, robots (mobiles, manipulators or mobile manipulators), entities (obstacles, objects, etc. ) and places (doors, walls, workrooms, corridors, etc. ) are tagged with RFID tags. Tagging such an entity (operators, robots, places or entities) involves sticking an RFID tag on it. In this paper, tagging an object with fiducial consists of adding a colored fixed point in the image of the environment (as shown in Fig. 6 ), and making a database entry mapping the tag ID to all the object information.