dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion This paper has covered the main novelties of Isabelle/PIDE and its Isabelle/jEdit application from the past 2 years. The total lifespan of the project so far has been approx. 6 years. This relatively late stage is characterized by cumulative and incremental improvements, towards a more and more consolidated prover interface that users find hard to avoid. With the "self-application" of the IDE to develop ML modules of Isabelle itself, the name of integrated development environment is really justified. Ambitious users may even invent their own add-on languages within PIDE documents, and apply the same principles of continuous editing and processing with rich markup. This can be done with or without connection to the formal content of Isabelle theories, as demonstrated by the IDE for Standard ML that is now included in Isabelle2014.
Step 3: Calculate the consistency ratio of a matrix Ã using the mathematical formula CR= CI/RI as explained by Saaty  [10]. A CR of 10% or less is acceptable. Step 4: Normalize weight vector in order to determine the local and total importance weights (Wi). Following the above explained steps, the MATLAB package is used to calculate the priority weights of the main aspects, the criteria, and the sub -criteria, (see Table  1. ) and then the results are analyzed in the third section.
       A x i i i i x a P W A a ) , ( ) 1 /( 1 ) ( 1  (6)        A x i i i i a x P W A a ) , ( ) 1 /( 1 ) ( 1  (7) ) ( ) ( ) ( a a a        (8) The Visual PROMETHEE software includes the basic of PROMETHEE method is used to calculate outranking flows and rank the alternatives.
Moreover, in the social aspect "Customer satisfaction" is the most significant subcriterion that has to be considered by 3PRL providers. Then the 3PRL providers are evaluated regarding each sub-criterion in order to construct the evaluation table. By using the PROMETHEE application software "Visual PROMETHEE 1. 4", the outranking flows of 3PRL providers were obtained, as well as a suitable provider selected. The alternative 3PRLP₁ and 3PRLP₄ are incomparable as per PROMETHEE I but 3PRLP₁ is superior to 3PRLP₄ according to PROMETHEE II that gives the ranking in the preference order of RLP₃≻ RLP₂≻ RLP₁ ≻RLP₄ as shown in Fig. 2.
Data collection Data collection for this study started with an application to Economic Planning Unit The Ministry of Education of Malaysia gave their permission to evaluate the performance of schools in the three northern states: Kedah, Penang, and Perlis. Overall, this included 303 secondary schools. In order to assess the performance of schools before and after the implementation of the PPSMI policy, our analysis involved four cohorts of student. Two of these  (2003 -2005 and 2004 -2006)  were educated at the upper secondary level and took their PMR and SPM exams before the introduction of the policy. The other two cohorts,  2005 -2007 and 2006 -2008 , were educated and took their exams entirely under the new policy. Out of all schools in the three states only 237 had complete data on the four cohorts.
Inputs The first five inputs represent the teaching provision in mathematics and science subjects. Based on the available data, these inputs are measured as the number of classes provided by a school in a specific subject. The class size typically varies between 25 and 30 students but on some occasions may be outside this range. In the Malaysian education system each class is supported by 1. 5 teachers. Multiplying the number of classes in a subject by 1. 5 would produce an accurate estimate of the number of teachers involved -but technically would be just a data rescaling exercise and result in an equivalent model. Our data set also showed the number of teachers employed by the school. We decided not to use this data because teachers often teach several different subjects and may have an uneven teaching load.
Analysis of efficiency The first step of our analysis involved the assessment of output radial efficiency of all 221 schools in our sample, separately for each of the four years 2005 -2008. For example, the efficiency of schools in year 2005 was assessed using the data for cohort 2003-2005. Similarly, the efficiency in year 2006 was evaluated based on the data for cohort 2004-2006. We performed computations in the HRS technology described above. For reference purposes, we also computed the output radial efficiency of schools in the VRS and CRS technologies. All computations were programmed and executed in a commonly available linear optimiser. The computation results are summarised in Table  2. < Table  2  here > 5 The description of VRS technology is obtained from conditions (2), where  and  are zero vectors.
e. f [s]  i j = f for every i, j and s). A software package, called BLUEFIN 1 , was specifically prepared to study all these issues. Within this package, numerical minimizations are performed using the MINUIT  [12]  libraries through their integration in ROOT  [13] , imposing the constraints that scale factors remain between 0 and 1. All scale factors are varied in the minimization, except those which are known to have no effect on information because the information derivatives with respect to them (which are essentially those presented in Sec. 4. 3) are zero both at "nominal" and at zero correlations (i. e. when all scale factors are 1 and 0, respectively). The "ByOffDiagElem" minimization is the most tricky, as it may trespass into regions where the total covariance matrix is not positive definite, sometimes in an unrecoverable way, in which case we declare the minimization to have failed.
(σ [s] A(cor) ) 2 (σ [s]  B(cor) ) 2 (σ [s]  C,cor ) 2 (σ [s]  C,cor ) 2. (σ [s]  A(cor) ) 2 (σ [s]  B(cor) ) 2 (σ [s]  C,cor ) 2 (σ [s]  D,cor ) 2.          . ( 46 ) Not surprisingly, in the simple example presented in Sec. 2, the onionization prescription gives the same result as the three minimization procedures described above (i. e. ρσ B1 σ B2 = σ 2 B1 = 16. 00), leading to a combination where λ B2 = 0 and again the less precise measurement B2 is essentially excluded. A more complex example is presented below. Note that, in the procedure described above as well as in its implementation in the BLUEFIN software that we used to produce the results presented in the next section, we systematically apply the "onionization" of the partial covariance matrix for each source of uncertainty.
In particular, note in Table  5  that the onionization procedure (but the same is true for minimizations) affects correlations for the BKGD and LUMI error sources in exactly the same way without distinctions. If this was a real combination, instead, one would most likely keep the LUMI correlation unchanged (because a common luminosity measurement would indeed result in a 100% correlation between y A , y B and y C , and these three measurements together could even help to constrain the error on it), concentrating instead on the re-assessment of the BKGD correlation alone (because the initial "nominal" estimate of 100% correlation is neither conservative nor realistic in the presence of different sensitivities to differential distributions). It should finally be added that the total covariance matrix derived from the onionization prescription is used as the starting point of the "ByOffDiagElem" minimization in the BLUEFIN software, as we have found this to improve the efficiency of the minimization procedure.
Acknowledgements This work has been inspired by many discussions, during private and public meetings, on the need for critically reviewing the assumptions about correlations and the meaning of "weights", when combining several measurements in the presence of high correlations between them. It would be difficult to mention and acknowledge all those colleagues who have hinted us towards the right direction and with whom we have had very fruitful discussions. We are particularly grateful to the members of the TopLHCWG and to the  AT-LAS  and CMS members who have helped in the reviews of the recent top mass combinations at the LHC. We would also like to thank our colleagues who have sent us comments about the first two public versions of this paper. In particular, it is a pleasure to thank  Louis Lyons  for his extensive feedback and his very useful suggestions.
Calculation of an Illustration by Using Proposed Algorithm. We run the experiments by software Matlab (R2009a) on a personal computer with Intel Core 2. 2 GHZ and 4 G RAM. First we test crisp numbers by using Algorithm 1 and then compare them with  [6, 7]. The inconsistent matrix in  [6, 7]  is the following matrix: 𝐴 = ( ( ( ( ( ( ( ( ( ( ( ( 1. 0000 5. 0000 3. 0000 7. 0000 6. 0000 6. 0000 0. 3333 0. 2500 0. 2000 1. 0000 0. 3333 5. 0000 3. 0000 3. 0000 0. 2000 0. 1429 0. 3333 3. 0000 1. 0000 6. 0000 3. 0000 4. 0000 6. 0000 0. 2000 0. 1429 0. 2000 0. 1667 1. 0000 0. 3333 0. 2500 0. 1429 0. 1250 0. 1667 0. 3333 0. 3333 3. 0000 1. 0000 0. 5000 0. 2000 0. 1667 0. 1667 0. 3333 0. 2500 4. 0000 2. 0000 1. 0000 0. 2000 0. 1667 3. 0000 5. 0000 0. 1667 7. 0000 5. 0000 5. 0000 1. 0000 0. 5000 4. 0000 7.
A galaxy model is a multidimensional model dedicated for XML document warehouses. It can be seen as a network of entities (i. e. , dimensions) connected via nodes. After giving an overview of our four-steps semiautomated method for the generation of galaxy models which aims to build data marts from XML documents. This paper focuses on the software tool, called Galaxy-Gen that implements the proposed method. We illustrate the Galaxy-Gen functionalities and make its first assessment through two experiments. The first experiment is applied to a set of twenty XML documents taken from the academic domain. The second one addressed a set of 1691 XML documents issued from the Clef-2007 collection. The assessment is performed by comparing manual design galaxy models with those produced by the Galaxy-Gen tool.
INTRODUCTION The organization's documents help decision makers to understand how corporate data evolve over time. Thereby, these documents represent an important volume that should be incorporated into the decision support system. However, so far, decisional analyses are based on multidimensional databases which mainly store numeric business indicators issued from OLTP (On-Line Transaction Processing) systems. In practice, these numeric data represent less than the quarter of the whole volume of data that could be useful for decision makers. Time is coming to focus on non-numeric data stored in documents; these data are important for the decision making process. Consequently, during this process some relevant documents may be ignored while some non pertinent documents can be considered by intuition.
Data-centric documents contain structured data (e. g. , order, invoice) as data strored or issued from databases. While, the document-centric XML documents are text-rich and then less structured (e. g. , scientific articles, company reports). Furthermore, an XML document is generally compliant to a generic grammar called DTD "Document Type Definition" or XSD "XML Schema Definition". In our work, we are interested in XML document-centric documents. For this latter, there are two categories of approaches for document warehousing: contextualization of the data warehouse with XML documents  (Pérez and al. , 2008) , and construction of data mart from the metadata of documents  (Krouf, 2004)   (Tseng and Chou, 2006). In general, even if they belong to the same domain, XML documents may have different structures.
In this paper, we focus on the second method that produces a multidimensional galaxy model for the XML DocW. More precisely, we tackle the experiments and evaluation of this method on academic and medical collections, through our developed software tool called Galaxy-Gen (Galaxy Generation). This paper is organized as follows: In Section 2, we discuss related works that treat multidimensional modeling of documents. In Section 3, we give an overview of our approach for building the schema of the XML document warehouse. Then, our multidimensional modeling method is described in Section 4; while Section 5 shows the functionalities of the Galaxy-Gen software tool. Finally, in Section 6, we conclude the paper and address future works.
, 2002)  and  (Yoo and al. , 2005) , since it is graphical and easy to be understood by unskilled persons. Secondly, Generation of unified trees step produces a limited number of unified trees. It treats both acronym and synonym ambiguities of tree nodes, referring to a dictionary of acronyms and the lexical database Wordnet. Then, it computes a triangular Similarity Matrix (SM) which has n trees in rows and in columns. It facilitates the identification of trees to be merged. Each pair of trees having their similarity factor higher than a given threshold (experimentally determined) is merged applying fusion-operators developed in  (Ben Messaoud and al. , 2011a).
EXPERIMENTS AND EVALUATION In order to substantiate our method, we have implemented a software tool named Galaxy-Gen for the generation of multidimensional galaxy model. This generation applies a set of rules (cf. , Section 4). It receives as input an XML structure or a unified tree resulting from the unification of a set of DTD and/or XSD belonging to the same domain and then produces a multidimensional Galaxy model. We have carried out two experiments: one on academic documents and other using medical documents.
Experiment on academic collection This first experiment is applied to a set of twenty XML documents taken from the academic domain and compliant to four complex DTDs (cf. Figure  5 ). In the remaining of this section, we present the features of our software tool Galaxy-Gen while exemplifying them through the galaxy-model generated for this first experiment. Since the input XML documents are described by heterogeneous we invoke our USD tool (Unification of Structures of XML Documents)  (Aoubed and al. , 2012)  in order to generate the unified tree for the four DTDs. After that, the galaxy generation process starts with picking the unified tree(s) for which the user wants to get a Galaxy model. Secondly the pretreatment step is launched to produce the pretreated tree shown in Figure  6. In this tree, cardinalities are automatically added for each parent node (except for the root), one cardinality for each outgoing edge.
CONCLUSION Documents represent an important source for decisional analyses. They merit to be integrated in the decision support system. In this paper, our main interest was to build the schema of the document warehouse. More specifically, we gave a detailed overview of the semi-automated method for the construction of the multidimensional model for a document warehouse. We have elected the Galaxy model to represent this multidimensional model. Likewise, we have presented a software tool, called Galaxy-Gen that implements the method for the generation of galaxy models. Furthermore, we have conducted two experiments using the Galaxy-Gen tool; they are to evaluate our proposals. The first experiment is applied on a set of XML documents taken from the academic domain. It produces a galaxy model composed of four dimensions.
The double role of scientific software The dominant role of software in our lives is the role of tools. A computer program does something: play videos, manage bank accounts, simulate protein dynamics, etc. A software is developed explicitly for doing something, and is evaluated by how well it performs the task. In most situations where software is used, there is a clear distinction between the software as a tool and the content that the tool works on. A video player is distinct from the movies it plays, and this distinction is visible to everyone: there is one file on the computer for each movie, and one file (or set of files) for the video player. The same video player can play many movies, and for each movie file there are multiple computer programs that can play them. The same clear distinction holds between the software that manages bank accounts and the databases that contain the actual data.
Creating such presentations on top of executable models ensures consistency between explanations and applications. With models being digital objects with clear semantics and a stable reference through a DOI, they become accessible to content mining and bibliometric analysis. It will be possible to compile databases of models used in published studies, which can then be annotated with validation information. The output of science shown in Figure  1  will become more formalized than it is now, which is likely to improve the quality of science overall. Another important gain in reliability can be expected from software technology. The automatic program verification methods that are currently developed (see e. g. 23 for a non-trivial practical application) will become available for scientific software 24.
On the previous page, however, we have the example of a description of the solar system as a set of differential equations. This seems to be crucially dependent on calculus. Is it therefore not scientific? F1000Research system as a set of differential equations. This seems to be crucially dependent on calculus. Is it therefore not scientific? As this example shows, the paper is sure to provide food for thought and for debate. We find that it does contribute to highlighting and clarifying the problems we encounter in computational science, and as such it deserves to be published and to reach a wider audience. There is one error which must be corrected before that, however: namely, on page 3, where the functions "sin" and "cos" are said to be uncomputable, by virtue of being transcendent. In fact, it is well known that they are computable (as are all functions that can be expressed by power series, which is one of the examples in Turing's 1936 paper).
5256/f1000research. 4790. r5205 I approve of the changes made to the article, and have no further comments. I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. No competing interests were disclosed. Competing Interests: Referee Responses for Version Patrik Jansson Cezar Ionescu Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden Approved: 27 May 2014 27 May 2014 Referee Report: doi:10. 5256/f1000research. 4263. r4684.
1 , 1. 5256/f1000research. 4790. r5205 I approve of the changes made to the article, and have no further comments. I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. No competing interests were disclosed. Competing Interests: Referee Responses for Version Patrik Jansson Cezar Ionescu Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden Approved: 27 May 2014 27 May 2014 Referee Report: doi:10. 5256/f1000research. 4263. r4684.
Strengthened milestones The milestones attached to Module 2 were weak, subjective and relied upon a self-declaration by the participant prior to Module 3 that the data collection had been completed. The milestones related to Module 2 were modified and made more objectiveone to be met within two weeks of Module 2 (submission of a plan for data collection, electronic data capture formats in EpiData (http://www. epidata. dk) and dummy tables and figures to facilitate analysis and reporting). The second milestone at least six weeks prior to Module 3 included submission of proof of study completion including the dataset and a draft analysis. not able to complete the research project in time due to changing her place and institution of work. The key advantages and challenges of the revised structure, as mentioned in the end-of-module feedback of participants and facilitators are summarized in the Table  2.
Main outcome measures The primary outcome was the proportion of results posted on ClinicalTrials. gov at three months. The secondary outcome was the proportion posted at six months. In a second step, two assessors blinded to the intervention group collected the date of the first results being received on ClinicalTrials. gov. A post hoc sensitivity analysis excluding trials wrongly included was performed. Results Among 379 trials included, 190 were randomized to receive the email intervention. The rate of posting of results did not differ at three months between trials with or without the intervention: 36/190 (19%) v 24/189 (13%), respectively (relative risk 1. 5, 95% confidence interval 0. 9 to 2. 4, P=0. 096) but did at six months: 46/190 (24%) v 27/189 (14%), 1. 7, 1. 1 to 2. 6, P=0. 014.
Introduction 2] [3] [4]  To overcome this problem, several initiatives were implemented. 6] [7]  On 27 September 2007, the US Food and Drug Administration Amendments Act section 801 (FDAAA 801) added a policy about the posting of main results on the register ClinicalTrials. gov, called "basic results," for all "applicable clinical trials" no later than one year after the primary completion date-that is, the date of collection of primary outcome data for the last patient to be enrolled. 8 pplicable clinical trials include phase II to IV interventional controlled trials registered after the enactment of the FDAAA 801 (or ongoing at this date) involving drugs, biologic agents, or devices (only after FDA approval for any use) regardless of sponsorship and involving at least one US site, whatever the primary country of origin of the trials.
Trial design We identified a cohort of all trials registered on ClinicalTrials. gov that did not comply with the FDAAA 801 requirements for posting trial results. We randomly selected a sample of these trials to receive an intervention. The proportion of trials with basic results posted was assessed for the whole cohort and compared between trials receiving the intervention and the rest of the cohort. This design was similar to the "cohort multiple randomized controlled trial," proposed elsewhere. 16 he only difference was that we included trials rather than patients. The random sample was determined by a computer generated randomization list, developed by an independent statistician who used R software. 17 Allocation was concealed because the randomization was implemented automatically by a web based system.
Study population Our cohort included all phase IV trials meeting the FDAAA 801 requirements for which basic results were not posted on ClinicalTrials. gov one year after completion of the trial and that had available contact details (email addresses) of responsible parties. Because the identification of those trials under the FDAAA 801 regulation is difficult,  13  we restricted our study to those registered as phase IV trials, which by definition are of FDA approved treatments for approved indications at approved doses. We first selected all trials that were closed for recruitment, interventional, phase IV, and with at least one site in the United States, by searching ClinicalTrials. gov in the "Advanced research" section. The search was performed on 20 August 2012. We then automatically downloaded all data referring to the selected trials from ClinicalTrials.
gov. One of us (AM) screened all trials records and excluded studies that appeared on ClinicalTrials. gov as incomplete or withdrawn; whose intervention was not a drug, a biologic, or a device; results already posted on ClinicalTrials. gov; and completion date less than one year. We thus included trials with an actual primary completion date (that is, the date when the final participant was examined or received an intervention for the purposes of final collection of data for the primary outcome) between October 2008 (one year after the date of application of FDAAA 801) and March 2011. In the screening we considered data as they were registered on the website ClinicalTrials. gov, without further verification. We extracted the email addresses of responsible parties (sponsors or principal investigators, or both) from ClinicalTrials.