dataesr/scientific-paragraphs-categorization huggingface dataset:
This is why we de-cided to make them work together (expert e4 ). The experience exchange that took place (exchange that the experts found to be constructive -see Section 4 ), have resulted in a consensus. With the resulting data of this consensus, the Choquet integral does provide a ranking of the actions for 6 of the 10 config-urations studied. Three rankings were obtained: In the second position, behind a 2 , we have either a 4 or a 5 ; action a 1 always occupies the last position; and, a 3 always occupies the last but one position. However, the position of a 3 appears to be very sensitive to the number of blank cards (see Section 5. 3. 4 ). Indeed, it is sufficient to add a single blank card in certain intervals to rank a 3 either in the third position or even in the second position tied with a 4.
Introduction: Privacy and the Privacy Paradox Most people shut their curtains, and don't live in glass houses -at least, not without the aforementioned curtains. We might like to watch Big Brother, but that doesn't mean we all want to participate. And yet, for various reasons -time, efficiency, lack of choice, lack of awareness, and so on -we intentionally sometimes and unintentionally oftentimes leak or broadcast information to whomever might be listening. Certainly, it's possible to adjust our privacy settings on social networks and so forth, but in reality, the tools we use and the environments, such as smart cities, we exist in, thrive on sharing data around and, crucially, about us. A perhaps more pernicious problem is that the people around us may well be either unaware or uncaring about the effects that their own behaviour with respect to privacy and sharing can have on the world around them.
Acknowledgments The cost of the GigaTracker development and construction were supported by the funding agencies of the  GigaTracker group Institutes. We are particularly indebted to:  F. R. S. -FNRS  ( Fonds de la Recherche Scientifique -FNRS ),  Belgium ,  INFN  ( Istituto Nazionale di Fisica Nucleare ),  Italy  and  CERN (European Organization for Nuclear Research) ,  Switzerland. We are grateful to the whole NA62 Collaboration for operating the detector during data taking and providing the data used to evaluate the spectrometer performance.
Acknowledgements The authors are very grateful to the anonymous reviewers for their helpful comments and suggestions on revising the paper. This work has been partially supported by the grants  MTM2013-41168-P ,  AP2010-0268  (D. P. ),  MTM2013-41168-P ,  MTM2016-80117-P  (G. G. ), and  MTM2012-31714 ,  2014SGR504  (J. J. M. ).
* Supported by  NSF  grant  DMS-1550991  and  US Army Research Office  Grant  W911NF-16-1-0404. †  Partially  supported by  ANR  project  Stint  under reference  ANR-13-BS02-0007  and by the  LABEX MILYON  ( ANR-10-LABX-0070 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). subdivision of K 4 as a (not necessarily induced) subgraph.
Genetic maps order genetic markers along chromosomes. They are, for instance, extensively used in marker-assisted selection to accelerate breeding programs. Even for the same species, people often have to deal with several alternative maps obtained using different ordering methods or different datasets, e. g. resulting from different segregating populations. Having efficient tools to identify the consistency and discrepancy of alternative maps is thus essential to facilitate genetic map comparisons. We propose to encode genetic maps by bucket order, a kind of order, which takes into account the blurred parts of the marker order while being an efficient data structure to achieve low complexity algorithms. The main result of this paper is an O(n log(n)) procedure to identify the largest agreements between two bucket orders of n elements, their Longest Common Subsequence (LCS), providing an efficient solution to highlight discrepancies between two genetic maps.
The LCS of two maps, being the largest set of their collinear markers, is used as a building block to compute pairwise map congruence, to visually emphasize maker collinearity and in some scaffolding methods relying on genetic maps to improve genome assembly. As the LCS computation is a key subroutine of all these genetic map related tools, replacing the current LCS subroutine of those methods by ours -to do the exact same work but faster-could significantly speed up those methods without changing their accuracy. To ease such transition we provide all required algorithmic details in this self contained paper as well as an R package implementing them, named LCSLCIS, which is freely available at: https://github. com/holtzy/LCSLCIS.
Individual maps can be compared based on their correlation coefficients, for marker interval distance and for marker order, based on their LCS using the qualV package  [7]  as done for comparing switchgrass maps across studies  [8, 9]. A visual representation of pairwise map collinearity is very helpful and several tools, such as MCScanX  [10] , VGSC  [11]  and the genetic map In linkage maps, some markers may have the exact same position on a given map due to the absence of recombination events between them. In physical maps, this can happen when different genetic markers match at the same place. https://doi. org/10. 1371/journal. pone. 0208838. g001 comparator  [12] , provide the so called "dual synteny plot representation". This representation draws the two maps side by side and traces a line between their common markers.
In the next section, we present bucket orders and propose two definitions of longest common subsequence for bucket orders (LCS and LCIS). Then, we give details of procedure of homogenization used to allow bucket orders to behave like total orders in terms of time complexity. In the following section, we prove that searching for the LC(I)S of two bucket orders gives the same results as searching for the LC(I)S of their homogenized counterparts. Afterwards, we describe the algorithm which solves the problem, and give a variant to achieve the O(n log(n)) time complexity. Finally, in the last section, we briefly show an application of this work in the framework of dual synteny plots and we use simulated map datasets of various sizes to demonstrate the gain in speed thanks to our optimized LCS routine.
As the two compared solutions are guaranteed to return an optimal LCS, the solutions proposed by the two approaches are equally good and the only difference between these methods is the time they need to return the searched LCS. To emphasize the speed up brought about by our solution, we simulate pairs of bucket orders containing 100, 500, 1000, 5000, 10000, 50000, 75000 and 100000 markers. The first order of each pair is obtained by randomly assigning its n markers to n/10 buckets, while the second order is obtained by swapping 10% of the buckets and moving randomly 10% of the markers (the simulation script is available on LCSLCIS github repository). On the laptop used to conduct the tests (intel i7-6600U, 16Gb RAM), our LC(I)S solution can easily handle datasets of 100,000 markers in seconds whereas the qualV LCS implementation is unable to handle datasets containing 50,000 markers.
Introduction Computing whether a graph H appears as a "pattern" inside some other graph G is a well-studied problem in the area of structural and algorithmic graph theory. Kwon is supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC consolidator grant DISTRUCT, agreement No. 648527). Dabrowski and Paulusma are supported by EPSRC (EP/K025090/1) and the Leverhulme Trust (RPG-2016-258). Jeong and Oum are supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2017R1A2B4005020). Kanté is supported by French Agency for Research under the GraphEN project (ANR-15-CE-0009). Underlying research data: source code used to prove Propositions 6 and 10 can be found at  [6].
In our approach, seven sections were first automatically detected in the full text. The accuracy of this classification into sections was above 80% compared with a dataset of documents where sentences were assigned to sections by experts. Second, each section was weighted according to the questionnaire results. The GEM score was then calculated as a sum of weights of sections in the full text corresponding to sentences in the abstract normalized over the total sum of weights of sections in the full text. The correlation between GEM score and the mean of the scores assigned by annotators was higher than the correlation between scores from different experts. As a case study, the GEM score was calculated for 36,237 articles in environmental sciences  retrieved from the French ISTEX database. The main result was that GEM score has increased over time.
Schematically speaking, a generous abstract should have the best possible score of similarity with the sections that are important to the reader; sections must therefore be weighted according to their importance in the calculation. Matching sentences from the abstract with those issued from the full text was inspired by the work of  (Atanassova et al. , 2016) , who aimed to compare abstract sentences with sentences issued from a full text. Our study aims to answer the following research questions: 1) Is the abstract a teaser rather than an exact reflection of the article content? By teaser we mean a promotional device or advert intended to arouse interest or curiosity for what will follow. 2) Are authors who write generous abstracts also generous in providing open access to their work? 3) Has generosity of abstracts evolved over time in the case study field of environmental sciences? These are the questions addressed in remainder of this paper using text-mining techniques and the voluminous database available from ISTEX, combined with the results of an online questionnaire.
In the Related Work section, we clarify the motivation for the work presented and situate the focus of our research. The Materials and Methods section includes the constitution of a dataset of 36,237 articles in the environmental sciences and details the two approaches chosen: on one hand, an online questionnaire on researchers' practices and their relationship with the abstract; on the other hand, the definition of the automatic metric GEM (for GEnerosity Measure) that calculates an abstract's generosity. The Results section presents evaluations of the section classification tool and GEM score. Finally, we conducted an experiment aiming to apply GEM to the defined dataset.
, 1999). In the field of biomedicine, some studies for drug target discovery  (Kafkas et al. , 2017)  integrated full texts and abstracts into a massive database, successfully mining more than 26 million abstracts and about 1. 2 million full texts for 1. 1 million target-drug discoveries. However, when considering paragraph-sized segments of full text articles, searching performed on abstracts only is shown to be far less efficient. Using their own technology to compare 23 million PubMed abstracts and 2. 5 million full text biology articles, Elsevier  (2015)  showed that more relevant and interesting facts are retrieved from a full text corpus than one containing abstracts alone. More recently, with a similar corpus and methodology,  Westergaard et al. (2017)  came to the same conclusion. In fields other than biology,  Klein et al.
Dataset Our analysis was based on a corpus of articles in the field of environmental sciences published from 1930 to 2013. This corpus was obtained from the Excellence Initiative for Scientific and Technical Information (ISTEX) database 4. ISTEX provides the French higher education and research community with online access to scientific archives in all disciplines. At the time of writing of the present article, this archive contains collections of scientific literature from all disciplines, covering journal archives, digital books, databases, text corpora, etc. from the following publishers: Elsevier, Wiley, Springer, Oxford University Press, British Medical Journal, IOP Publishing, Nature, Royal Society of Chemistry, De Gruyter, Ecco Press, Emerald, Brill, and Early English Books Online. The ISTEX platform provides a set of services via an HTTP-based web Application Programming Interface (API) 5  within a RESTful (REpresentational State Transfer) paradigm, i.
e. , the platform allows access and manipulation of textual representations of resources using a uniform and predefined set of stateless operations. A Graphical User Interface (GUI) is also available as a form of demonstration 6. The API enables to search for documents and their metadata. Search results and document metadata in JSON or MODS formats are available on open access, while access to retrieved documents is restricted and requires authentication. Documents are available in the following formats: • PDF (full text); • TEI (full text and enrichments); • XML provided by a publisher; • Different formats  (images, videos, sounds, etc. )  corresponding to appendices and publication covers. We retrieved 66,518 articles (tagged as research-article or article in the ISTEX database) categorized by ISTEX as "Environmental Studies" or "Environmental Science" (according to the Web of Science classification).
We selected articles for which we could retrieve a full text and an abstract from the PDF file. Out of the 59,419 article/abstract pairs thus obtained, we then chose to filter out documents having less than four section classes in the full text: 23,181 articles were therefore considered unsuitable for further analysis. The definitive dataset was composed of 36,237 articles (see published dataset of results in  Bordignon and Ermakova, 2018). Online Questionnaire An online questionnaire was designed to analyze the way in which a sample of researchers read and write abstracts. The questionnaire was developed on the basis of a broad definition of the abstract, which is divided into seven sections. The following definitions of abstract section classes were provided in the questionnaire:.
This online questionnaire was completed by 630 individuals between 08/24/2016 and 09/27/2016, to whom these definitions were presented. The large majority of respondents are researchers: 50% are PhD students or postdocs, 43% are professors or permanent researchers. Interviewees were asked to provide a maximum of two disciplinary fields (from a list of 12) that characterize their research. We asked the respondents to rank the seven sections in their respective fields according to the following scale: essential, important, marginal, optional or unusual, or unknown. We also asked if a good abstract is more like a summary or a teaser. They had the opportunity to send us one or two abstracts that they consider successful, and examples of journal names whose abstracts they consider satisfactory (see published dataset of answers;  Bordignon and Noël, 2018).
Article Section Detection The first step of our algorithm is section detection by GROBID software 8. • Header extraction and parsing from articles in PDF format (e. g. , extraction of title, abstract, authors, affiliations, keywords, address, etc. ); • Reference extraction and parsing from articles in PDF format, including references in footnotes, isolated references, and patent references; • Parsing of dates; • Full text extraction from PDF articles with document segmentation. Extraction and parsing algorithms use the Wapiti CRF (Conditional Random Fields) library 9. Wapiti is a toolkit for segmenting and labeling sequences with discriminative models based on maximum entropy Markov models and linear-chain CRF. GROBID is available in batch mode, as well as RESTful and JAVA APIs. We integrated GROBID in our tool using JAVA API.
Section Classification Evaluation Section classification evaluation was performed over a dataset annotated manually. For manual evaluation, we chose 20 documents at random. For each article, each sentence was tagged by two experts who are both researchers. The first of these experts has expertise in chemistry and the other has experience in economics and environmental sciences. We treated about 4,000 classified sentences. The quality of our classification algorithm was evaluated by a commonly used metric, namely accuracy. Accuracy of our classification was calculated as the number of correctly classified items over the total number of items and was found to be above 80%.
GEM Score Evaluation We conducted three types of experiment to evaluate the GEM score. In the first evaluation experiment, we hypothesized that the score assigned to the abstract of a given article should be higher than the score of the abstract coming from another article. Thus, we compared the score assigned to the original abstract with the scores of all other abstracts from the test set. We obtained 25% errors, i. e. , in 25% of cases the scores of abstracts corresponding to other articles were higher than the scores of the original ones, while the random score produced 55% of errors on the same dataset. In all cases, the errors of GEM were produced for non-generous original abstracts. We compared the GEM score with the scores assigned by the experts as in the previous subsection. Forty-two documents were annotated at least by one expert and 20 of these documents were assigned a score by both evaluators.
Experimental Results We calculated the GEM score for articles from the definitive dataset (n = 36,237) (see Figure  5 ). The most frequent GEM value, 0. 6447, occurred 4,683 times. As shown in Table  3 , this value was attributed to abstracts where three section types (INTRO, METHODS, and RESULTS) were detected in the abstract out of four found in the full text (OBJECTIVES was missing in the abstract). The second largest value (0. 4868) corresponds to detection of INTRO and RESULTS in the abstract while four section types are found in the full text (INTRO, OBJECTIVES, METHODS, and RESULTS). INTRO, METHODS, RESULTS, and CONCLUSION are section types that our algorithm looks for at the first stage. They are often organized as well-defined blocks of text in the articles. These results suggest that the sections INTRO, METHODS, and RESULTS are the most frequently presented in the abstract.
As Figure  6  shows for articles published in the last 40 years, we detected that abstracts tended to become more generous over time. We did not take the period 1930-1975 into account because of the small number of articles. The fall in the number of articles in 2002 shown in Figure  6  is inherent to the ISTEX database and more particularly to the end of data acquisition from Elsevier. The number of the remaining articles is still significant because it is above 500 articles a year. This fall in numbers had no effect on the growth of the GEM score over time. In order to illustrate the GEM score potential, we ambitiously propose additional analyses even if they appear to be premature. Nine publishers were identified in the definitive dataset (Table  4 ). Half of the dataset articles were published in an Elsevier journal.
We found significant differences between publishers: abstracts from Sage and Springer journals appeared to be less generous than those of other publishers (see Figure  7 ). These results need to be further investigated in order to identify whether the guidelines for authors or even instructions about structured abstracts could have impacted this trend. The environmental sciences dataset we tested also includes articles from journals categorized in one or more additional subject areas (according to the Elsevier journal classification). Table  5  shows the distribution among subject areas and Figure  8  compares the seven most important subject areas excluding environmental sciences that are obviously common to all articles. This provided an opportunity to compare GEM score between disciplines.
No significant differences were found except for the abstracts of articles in the social sciences (n = 3,494) which were the least generous. In the commentaries collected in our online questionnaire, we also came across views consistent with this conclusion: "In the field of literary studies, we do not have any abstract of this kind [. ]. I tried to answer your questionnaire anyway, but this type of publication is simply not part of our practice (we're talking about articles, codified but not as rigidly). " These results need to be investigated further, including making a comparison with a social sciences corpus that could also be retrieved from the ISTEX database. Finally, we used the oaDOI API 10  to look for open access versions of the articles. As far as we know, literature about openness and open access to publications does not deal with abstract content.
The questionnaire results provide also section importance weightings, a unique and very useful information. One of the possible improvements of the proposed measure is to revise the rules we used for section classification to include regular expressions. We also need to supplement the list of words used in the latter. Another means of improvement would be to learn section weights from an annotated corpus. This research does start the process of measuring the quality of an abstract. It could be taken further, in particular by exploiting structured abstracts that are included in the dataset. It would be interesting to calculate GEM scores for such abstracts, which have a structure imposed by the journals or publishers, and to compare them with those written without guidelines. Recommendation systems have emerged recently because document databases enable learning from usage.
The goal of this project is to manage to be a model in the creation and implementation of sMOOCs on a large scale, introducing mobile devices in the m-learning. The sMOOC "Step by Step" is given in six languages and is part of the European Project ECO (E-learning, Communication and Open-data), placed inside the Competitiveness and Innovation Framework Programme (CIP-ICT-PSP. 2013). This sMOOC relies on the participation of ten European institutions for higher education in six different countries and is coordinated by the Sorbonne Nouvelle University in France and the National University for Distance Education (UNED) in Spain. This sMOOC relies on a pedagogical design coherent with the requirements of the digital era as they expect participants to be co-responsible in their own learning. At the end of the sMOOC, the participants must have dealt with the following questions: Why do a sMOOC? How is a sMOOC built? How is a sMOOC designed? How to make a sMOOC accessible and successful? How is a sMOOC assessed and its data used? As such the sMOOC "Step by Step" is a "metaMOOC" (Osuna-Acedo & Gil-Quintana, 2017) since it appears as "an invitation to think about the role that massive online open training, especially sMOOCs, play in the 21st century, in any learning process"  (ecolearning, 2015; Gil-Quintana, 2016; Mañero-Contreras, 2016).
The Fletcher-Reeves conjugate gradient method: β k+1 = r k+1 T r k+1 (r k ) T r k (14) 2. The Polak-Ribière conjugate gradient method: β k+1 = max r k+1 T r k+1 -r k (r k ) T r k , 0 (15) To use conjugate gradient algorithm, we need the first derivative Ψ (µ)) = (∆ 1 ,. , ∆ i ,. , ∆ K ). Since no mathematical expression is available, it is approximated with finite differences  [10]. In our tests, we have used a centered difference approximation to compute the first derivative as follows: ∆ i = Ψ (µ 1 ,. , µ i + ε,. , µ n ) -Ψ (µ 1 ,. , µ i -ε,. , µ n ) 2ε (16) The good approximation of the first derivative relies on the choice of the value of the parameter ε. Through the tests conducted, we have selected 0. 01 as the best value. In practice, the application is implemented in the cross-platform Qt creator (C++) under Linux system.