dataesr/scientific-paragraphs-categorization huggingface dataset:
Building WiFi/GSM Signal Open Data Maps This second experiment illustrates the benefits of using APISENSE to automatically build two open data maps from datasets collected in the wild. Listing 1. 2 is a JavaScript script, which is triggered whenever the location of a participant changes by a distance of 10 meters in a period of 5 minutes. When these conditions are met, the script builds a trace which contains the location of the participant and attaches WiFi and GSM networks characteristics. From the dataset, collected by three participants over one week, we build an QuadTree geospatial index to identify the minimum bounding rectangles that contain at least a given number of signal measures. These rectangles are then automatically colored based on the median signal value observed in this rectangle (cf.
Detecting Exceptions Raised by User Applications The third experiment highlights that APISENSE does not impose to collect geolocated dataset and can also be used to build realistic dataset focusing on the exceptions that are raised by the participants' applications. To build such a dataset, Listing 1. 3 describes a CoffeeScript script that uses the Android logging system (logCat) and subscribes to error logs (' * :E'). Whenever, the reported log refers to an exception, the script builds a new trace that contains the details of the log and retrieves the name of the application reporting this exception. Once deployed in the wild, the exceptions reported by the participants can be used to build a taxonomy of exceptions raised by mobile applications. The Figure  5 , depicts the results of this experiment based on a dataset collected from three participants over one month.
Experimenting Machine Learning Models The fourth experiment does not only collect user-contributed datasets, but also deals with the empirical validation of models on a population of participants. In this scenario, the scientist wanted to assess the machine learning model she defined for detecting the activity of the users: walking, sitting, standing, running, or climbing and down stairs. To assess this model, she deployed a script that integrates two phases: an exploration phase and an exploitation one. To set up this experiment, we extended the scripting library by integrating a popular machine learning  [14]  and adding a new facade to use its features from script. The script (cf. Listing 1. 4) therefore starts with an exploration phase in order to learn a specific user model. During this phase, APISENSE generates some dialogs to interact with the participant and ask her to repeat some specific movements.
The script automatically switches to the next movement when the model has recorded enough raw data from the accelerometer to provide an accurate estimation. Once the model is considered as complete, the script dynamically replace the timer handler to switch into the exploration phase. The dataset collected by the server-side infrastructure of APISENSE contain the model statisitcs observed for each participant contributing to the experiment. 1 var classes = ["walk","jog","stand", "sit", "up", "down"]; 2 var current = 0; var buffer = new Array(); 3 var model = weka. newModel(["avrX","avrY",. ], classes); 4 var filter = "|(dx>"+delta+")(dy>"+delta+")(dz>"+delta+")";.
A direct impact of this property is that the execution of APISENSE scripts better preserves the battery of the mobile device compared to POGO, as it does not keep triggering the script when the user leaves the assigned polygon. Nonetheless, this statement is only based on an observation of POGO as the library is not made freely available to confirm this empirically. Evaluating the Energy Consumption. In this section, we compare the energy consumption of APISENSE to a native Android application and another state-of-the-art crowdsensing solution: FUNF  [1]. FUNF provides an Android toolkit to build custom crowdsensing applications à la carte. For each technology, we developed a sensing application, which collects the battery level every 10 minutes. We observed the energy consumption of these applications and we report their consumption in Figure  7.
Related Work A limited number of data collection tools are freely available on the market. SYSTEM-SENS  [8] , a system based on Android, focuses on collecting usage context (e. g. , CPU, memory, network info, battery) of smartphones in order to better understand the battery consumption of installed applications. Similarly, LIVELABS  [19]  is a tool to measure wireless networks in the field with the principal objective to generate a complete network coverage map in order to help client to select network interface or network operators to identify blind spots in the network. However, all these tools are closed solutions,   designed for collecting specific datasets and cannot be reused in unforeseen contexts in contrast to APISENSE. Furthermore, these projects are typical experiments deployed on mobile devices, without providing any privacy guarantee.
FUNF  [1]  is an Android toolkit focusing on the development of sensing applications. FUNF in a box is a service provided by FUNF to easily build a dedicated sensing application from a web interface, while data is periodically published via the Dropbox service. As demonstrated in Section 4, the current version of FUNF does not provide any support for saving energy nor preserving user privacy. Furthermore, the current solution does not support the dynamic re-deployment of experiments once deployed in the wild. More interestingly, MYEXPERIENCE  [9]  is a system proposed for Windows mobile smartphones, tackling the learning curve issue by providing a lightweight configuration language based on XML in order to control the features of the application without writing C# code. MYEXPERIENCE collects data using a participatory approach-i.
e. , by interacting with users when a specific event occurs (e. g. , asking to report on the quality of the conversation after a phone call ends). However, MYEXPERIENCE does not consider severals critical issues, such as maintaining the privacy of participants or the strategic deployment of experiments. Even if an experiment can be modified in the wild, each experiment still requires a physical access to the mobile device in order to be installed, thus making it difficult to be applied on a large population of participants. In the literature, severals deployment of crowdsensing applications strategies have been studied. For example, ANONYSENSE  [20]  uses-as APISENSE-a pull-based approach where mobile nodes periodically download all sensing experiments available on the server. A crowdsensing experiment is written in a domain-specific language and defines when a mobile node should sense and under which conditions the report should be submitted to the server.
However, ANONYSENSE does not provide any mechanism to filter the mobile nodes able to download sensing experiments, thus introducing a communication overhead if the node does not match the experiment requirements. On the contrary, PRISM  [7]  and POGO  [3]  adopts a push-based approach to deploy sensing experiments over mobile nodes. PRISM is a mobile platform, running on Microsoft Windows Mobile 5. 0, and supporting the execution of generic binary code in a secure way to develop real-time participatory sensing applications. To support real-time sensing, PRISM server needs to keep track of each mobile node and the report they periodically send (e. g. , current location, battery left) before selecting the appropriate mobile phones to push application binaries. POGO proposes a middleware for building crowdsensing applications and using the XMPP protocol to disseminate the datasets.
2. 1Cloud computing While cloud computing is currently a term without a single consensus meaning in the marketplace, it describes a broad movement toward the use of wide area networks, such as Internet to enable interaction between IT service providers of many types and consumers. According to  [12] , a cloud is a hardware and software infrastructure which is able to provide services at any traditional IT layer: software, platform, and infrastructure, with minimal deployment time and reduced costs. Along with this new paradigm, various business models are developed, which can be described by terminology of"X as a service (XaaS)"4, where X could be software, hardware, data storage, and etc. Examples includes Amazon's EC2 and S31, Google App Engine13, and Windows Azure16 which provide users with scalable resources in the pay as you use fashion at relatively low price.
Level of Detail The LoD technique has been widely used in 3D graphics and simulations. The basic purpose of the technique is to modulate the complexity of a 3D object representation according to the distance from which it is viewed (or any other criteria)  [15]. As introduced by James Clark  [6] , this technique is meant to manage processing load on graphics pipeline while delivering an acceptable quality of images. The technique suggests to structure the rendering details of a 3D object in order to optimize its processing quality if the object's visible details are proportional to the distance from which the object is viewed. The figure  3  presents the rational of Clark: by changing the number of vertices, we see the change in the quality and the visualization of a sphere. Geometric datasets are usually too large in data size and complex (in terms of time and computational resource demands) so their rendering can become a tedious and time consuming process.
Although the number of available allometric equations has increased significantly in recent years, estimates of biomass have not necessarily improved. Biomass equations had been compiled for tropical, subtropical, temperate, and boreal trees or forests  (Brown et al. 1989 , de Jong et al. 2009 , Henry et al. 2011b , Jenkins et al. 2003 , Návar 2009 , Schroeder et al. 1997 , Ter-Mikaelian & Korzukhin 1997). However, biomass equations for some ecosystems such as shrubland  (Nàvar et al. 2002)  or continents such as Africa  (Chave et al. 2005 ) remain understudied. In addition, the access to documents reporting tree allometric equations is limited. Most of them are found in the scientific literature to which access is restricted. Currently, national and continental databases for allometric equations are few  (Henry et al.
2011a , Návar 2009 , Zianis et al. 2005)  and should be continually updated. In addition, the existing databases are not harmonized @ @ Matieu Henry (matieu. henry@fao. org) GlobAllomeTree: international platform for tree allometric equations to support volume, biomass and carbon assessment  Matieu Henry (1) , Antonio Bombelli (2) , Carlo Trotta (3) , Alfredo  Alessandrini (3) , Luca Birigazzi (1) , Gael Sola (1) , Ghislain Vieilledent (4) , Philippe Santenoise (1) , Fleur Longuetaud (7) , Nicolas Picard (4) , GlobAllomeTree is an international platform for tree allometric equations. It is the first worldwide web platform designed to facilitate the access of the tree allometric equation and to facilitate the assessment of the tree biometric characteristics for commercial volume, bio-energy or carbon cycling.
The w bplatform presents a database containing tree allometric equations, a software called Fantallomatrik, to facilitate the comparison and selection of the equations, and documentation to facilitate the development of new tree allometric models, improve the evaluation of tree and forest resources and improve knowledge on tree allometric equations. In  he Fantallometrik software, equations can be selected by country, ecological zones, input parameters, tree species, statistic parameters and outputs. The c ntinuously updated database currently contains over 5000 tree allometric equations classified according to 73 fields. The software Fantallometrik can be also used to compare equations, insert new data and estimate the selected output variables using field inventory. The GlobAllomeTree products are freely available at the URL: http://globallometree.
org for a range of users including foresters, project developers, scientist, student and government staff. and data are often not comparable. Indeed, allometric equations correspond to different tree components (Fig. 1 ), different forest and management types, different intervals of validity and the estimated variables cannot be compared without preliminary alignment steps. The choice of the most accurate allometric equation for biomass estimation is difficult in two cases  (Henry et al. 2011a ). First, more than one allometric equation may be available for a tree species and for a given ecological zone. Second, there may be no allometric equation for a given species or ecological zone or the available equations could not be reliable. It is crucial to have access to al-lometric equation databases to ascertain these potential issues.
In both cases, several authors offer solutions taking into account the impact of the selected equation on carbon stocks and potential emission reduction calculations  (Henry 2010 , Melson et al. 2011). Software and computer programs have largely contributed to improving forest estimates and facilitating the procedures of calculation  (Masera et al. 2003 , Nagel & Schmidt 2003 , Porté & Bartelink 2002 , Vanclay 1994). Widely used in developed countries, these softwares are usually specific to a country or an ecological zone. Currently, no software accounts for the great diversity of allometric equations, national circumstances and specific ecological zone classification. The objective of this paper is to present the online platform "GlobAllomeTree" and our efforts to facilitate the development of a global tree allometric equations database and a software for volume, biomass and carbon assessment, using all existing source of allometric equations, but also to facilitate access to all the information related to tree allometric equation development.
An internet platform to facilitate access to tree allometric equations The GlobAllomeTree online platform provides: (1) a consistent and harmonized database of tree allometric equations for volume, biomass and carbon assessment of trees at the global scale; (2) a software to estimate the variables of interest such as volume, biomass and carbon stocks taking into account the existing tree allometric equations; (3) easy access to scientific research information on allometric equation and assessment of volume, biomass and carbon stocks; and (4) access to tutorials and manuals supporting the use and the development of tree allometric equations (Fig. 2 ). The platform is a unique and allows for the first time access to data that are generally difficult to access, either because they are in scientific journals, either because they are found in electronic documents or remote libraries.
The tools provided are complementary and allow users to implement various steps from the field measurement to the prediction of volume, biomass or carbon stock. Data are available for different types of vegetation, from mangrove to plantations, from individual trees to stands. The tools target a large number of users, both for the assessment of forest resources and trees outside forests. Among the main users are people working on forest resource assessment (bioenergy trade volume, carbon stocks), scientists working on the dynamics of forest biometrics, forest technicians, students, and project developers involved in management of natural resources, especially forests.
Tree allometric database to facilitate comparison and selection of allometric equation The web platform GlobAllomeTree provides free access to one global database for existing volume, biomass and carbon allometric equations for the different tree components identified in Fig. 1. The equations were georeferenced when possible from the original information. In some cases, it was not possible to access the original documents and the equations were not georeferenced. In the first version of the database, the number of equations is 706 for Europe (100% georeferenced equations), 2843 for North America (74% georeferenced equations), and 1058  for Africa (66% georeferenced equations -Fig. 3 ). Users can freely filter the database according to their needs (volume, biomass, carbon, edible foliage biomass, location, coun-try, ecological zone etc).
The software has been developed to cover many possible uses. The data were classified according to 5 ecological zone classifications (Holdridge, WWF, Bailey, FAO and Udvardy ecological classifications are available at: http://www. carboafrica. net/data_en. asp) and eleven tree components (Fig. 1 ), by population types (stand, individual tree, sprout), and by forest type (plantation, natural forest etc). An utmost care has been paid to avoid any transcription error from the primary literature to the figures reported in the database. However, may errors be detected, users are encouraged to send queries for updates and the database will be changed accordingly. Users are also encouraged to submit new allometric equations and databases to enrich the platform and contribute to the efforts.
A software to use the allometric equation database and assess volume, biomass and carbon stocks A software was developed to facilitate the assessment of the volume, biomass and carbon stocks in native and secondary forests, plantations, tree outside forests, bamboos, lianas and mangroves  (Trotta et al. 2013). The software was developed using the deploy tool made available for non-commercial use by Mathworks © (http://www. mathworks. it/help/toolbox/compiler/deploytool. html). Users can upload the existing allometric equation database made available on Glob-AllomeTree or develop their own. The software contains three main modules (Fig. 4):  (1) comparison of existing tree allometric  (2) assessment of volume, biomass and carbon stocks and automatically calculate from field measurement the volume and the biomass; (3) insertion of new allometric equations.
The software is developed to give the possibility to insert new data. The user should careful read the detailed on-line guidance before starting to install and make calculations  (Trotta & Henry 2012). The first module allows the user to filter equations according to his/her needs. The second module calculates the volume, biomass and carbon stocks using the selected equations. The user can import data from a field forest inventory plots and obtain the descriptive statistics for the allometric equations considered. The third module allows the user to automatically enter new equations that were not present in previous database. Users can submit entire databases or individual allometric equations.
Acknowledgements The  Food and Agriculture Organization of the United Nations (FAO) , the  Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD)  and the  Department for Innovation in Biological, Agro-Food and Forest System at Tuscia University (UNITUS-DIBAF ) jointly developed this internet platform taking profit of the experiences built from several projects. We thank the  European Union  for funding the research required to the development of the platform through  Carbo-Africa  ( 037132 ) and ClimAfrica (244240) projects. We also thank the  UN-REDD program , the  Agence Nationale de la Recherche  (via the  Emerge  project),  GeoCarbon  ( 283080 ) and Global Terrestrial Observation System for their support. We thank  Dr. Jérôme Chave  ( CNRS ) for his contribution.
The forces and moments were measured by the internal five-component Micro Sting Balance (Figure  3 ) which is limited to 10 N in terms of forces and 0. 5 N-m in terms of moments. This balance is made of high strength 35NCD16 alloy steel. The calibration of this balance used in the current investigation follows the same parameter as the calibration performed by Thipyopas in  [13]. The acquisition system for SabRe was developed by DAEP Laboratory and is composed of a National Instruments acquisition terminal, six Celians signal conditioners (power supply, signal amplification, filters), a control and acquisition computer and a National Instrument rack equipped with an interface SCXI module. A Labview based software was developed and used for the control of the experimental setup and for acquisition of the data.
ACKNOWLEDGMENTS 1st and 4th authors would like to thank the support of project by The  Thailand Research Fund ;  TRF's Grant  no. MRG5580235. The authors appreciate the help and contributions of  Rèmi Chanton ,  Sebastien Prothin  and other members of the DAEP SUPAERO with the experimental setup preparation. Finally, the authors would like to thank  Ryan Randall , his previous works helped and gave invaluable information. Kwanchai Chinwicharnam ,  David Gomez Ariza ,  Jean-Marc Moschetta  259 and Chinnapat Thipyopas Volume 5 • Number 4 • 2013.
e. can not be applied to handle prokaryotic gene families. Results: We propose a reconciliation method accounting for gene duplications, losses and horizontal transfers, that specifically takes into account the uncertainties in gene trees by rearranging their weakly supported edges. Rearrangements are performed on edges having a low confidence value, and are accepted whenever they improve the reconciliation cost. We prove useful properties on the dynamic programming matrix used to compute reconciliations, which allows to speed-up the tree space exploration when rearrangements are generated by Nearest Neighbor Interchanges (NNI) edit operations. Experiments on synthetic data show that gene trees modified by such NNI rearrangements are closer to the correct simulated trees and lead to better event predictions on average.
Following the works cited above to deal with uncertainties in the gene trees, we propose a heuristic method relying on NNI edit operations to search for a gene tree rearrangement that preserves strongly supported edges and minimizes the cost of reconciliation to a fixed binary species tree, but in the context of the more complex DTL model. The resulting dynamic program, called MowgliNNI, is a generalization of Mowgli  [2] , a program initially developed for fixed binary gene trees. Experiments on simulated data show that MowgliNNI provides a gene tree that is closer to the true evolutionary history of the gene family, and leads to more accurate D, T and L predictions. Experiments on real data show a significant decrease in the number of predicted events and an increased precision, that is a decrease in the number of equally most parsimonious reconciliations.
Next, we simulated the evolution of 1500 -3000 bp DNA sequences along this tree under the GTR model, thanks to the Seq-Gen program  [37]. The alignment, composed of one sequence per extant gene, was then given as input to RAxML  [38]  to obtain a maximum likelihood estimate of the gene tree, denoted G ML (also called initial gene tree below). Mowgli  [2]  and Ranger-DTL-D  [3]  were then used to infer a most parsimonious evolutionary history R ML , resp. R R , between this initial gene tree and the reference species tree S. Separately, MowgliNNI was used to search for an alternative Figure  6  The simulation protocol to obtain inferred gene trees from sequences derived from a true gene tree. The module "Deviation from ultrametricity" is taken from the program of Galtier that converts the edge lengths of ultrametric trees from the time unit into substitution numbers  [36].
This results from different factors among which the most important is that Ranger-DTL-D relies on a less general reconciliation model than Mowgli (e. g. not ensuring time consistency and not allowing gene loss in the donor branch after a transfer), but which on the other hand allows it to run at greater speed. As Mowgli and Ranger-DTL-D performed similarly, in the following we just report results obtained with Mowgli. MowgliNNI progressively reduced the number of predicted duplications, transfers and losses as the threshold increased. At threshold 0 (where MowgliNNI = Mowgli, 5510 duplications, 2494 transfers and 12190 losses were predicted on the whole dataset; going to threshold 80, these numbers dropped to 4602 duplications, 1676 transfers and 8133 losses, i. e. values that are much closer to the 4535 duplications and 8260 losses contained in the true reconciliations.