dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion and Future Work In this paper we elaborate on the subject of MLER by introducing a novel profile-based document translation method. We have set a number of research questions to this aim and our findings supported the following views: (1) According to our observations, although authors contribute almost similarly in multiple languages, considering all the contributions in different languages can be helpful for expertise retrieval system. Since authors usually repeat their contributions through languages, eliminating documents in out-of-the-context languages does not harm the retrieval performance considerably. (2) Document translation in MLER takes advantage of profile-based translation models. The profile of each expert helps us to opt for topical translations which usually contributes to correct translations.
Implementation The resulting segmenter for Catalan called DiSegCAT is then generated automatically from the segmenter for Spanish. These tools are implemented in Perl, and are based on regular expressions and the Twig XML library. Firstly, DiSeg calls the FreeLing library in order to apply the discourse markers grammar. This first module transforms the syntactic tree generated by the FreeLing Shallow Parser into XML format. The second module applies the rules that detect EDUs boundaries. This module reads all the leaves of the XML syntactic tree. When it recognizes a boundary, the syntactic tree is modified by adding a new node where the boundary is located. This task is iterated twice to find sub-EDUs boundaries inside the EDUs already detected. The third DiSeg module re-reads the new XML syntactic tree to split sentences into coherent EDUs that should contain one or more verbs.
These modules use regular expressions to recognize discourse markers, lemmas and grammatical categories. The idea was the development of a Perl script to translate the code for Spanish discourse segmentation to Catalan. This strategy presuppose that EDUs have the same grammatical structure and segmentation markers are the only elements that are altered. This assumption is not entirely true. There are discourse markers in Spanish that correspond to one or more discourse markers in Catalan. For example, the Spanish marker para ('to') is per ('to', 'by' or 'through') or en ('in') in Catalan. Furthermore, there are grammatical categories tags, such as 'vaux' for auxiliary verbs, that not have a correspondence in the grammar version of Catalan. Therefore, a lexicon that transforms the Spanish DiSeg to the Catalan DiSeg cannot handle these cases but contextual rules can contribute to solve these asymmetries between both languages.
The final implementation is the es2cat. pl perl script that translates the original DiSeg into DiSegCAT. As a consequence, any expansion of the original DiSeg is applied automatically to the Catalan version. This strategy relies on the idea that translating a NLP software is much more easier than translating texts written in natural language. Therefore, the results provided by DiSegCAT are more reliable than the original DiSeg combined with a machine translation system Catalan ↔ Spanish.
Evaluation We consider two baseline segmenters to compare our results: -Baseline 1 : it inserts boundaries before coordinating conjunctions. -Baseline 2 : it considers all complete sentences like discourse segments. This baseline will have a precision of 100%, because all detected segments will be correct, since sentences are considered discourse segments. The results obtained are shown in Table  2. Differences between DiSegCAT and Baseline 2 scores are all significant based on 12-fold t-test with p-value < 0. 05. Since there is not another discourse segmenter for Catalan we cannot compare our results with another system. This is a current situation when working on local languages. We did try to combine DiSeg for Spanish with mainstream available translators form Spanish to Catalan. But translation is an even more complex problem than discourse analysis and in the case of our corpus in Spanish, translators were not efficient with high rate of errors including over short multiword expressions.
However, we find that our results are similar to those obtained for other languages by using similar strategies of segmentation, such as for English (F-Score = 83%) and Spanish (F-Score = 80%). As it can be observed in Table  2 , our system (DiSegCAT) obtains the best F-Score (75%), in comparison with Baseline 1 (52%) and Baseline 2 (18%). As expected, Baseline 2 obtains 100% of precision, since it considers sentences as segments; however, it obtains only 10% of recall, since it does not detect intrasentence segments. Baseline 1 has 44% of precision and 65% of recall. Although this baseline detects correctly some segments (because coordinated clauses can be also discourse segments), both results (precision and recall) are worst than the results obtained by DiSegCAT. These results mean that our algorithm outperforms baselines including linguistic information.
The Proposed Framework Our framework for Latin and modern Italian language discrimination is composed of the following three steps: (i) script coding, (ii) texture analysis, (iii) clustering. Script coding adopts the approach previously introduced by Brodić et al. [4]. In fact, it demonstrated to be successful for solving a critical task of closely related language discrimination  [3]. In particular, given the text document as input, it maps each letter of the document to only four codes based on the corresponding position in the text line, representing the gray-level pixels of a 1-D image. Then, texture analysis is performed on the produced image in order to extract run-length texture features. In order to select the feature representation, three well-known types of texture features, run-length, co-occurrence and ALBP, have been evaluated on benchmark datasets of the same languages.
Experimentation As example of framework usage, an experiment is performed on a complex custom oriented database, publicly available at  [10] , composed of a set of 90 documents in Latin and modern Italian languages. Specifically, 50 out of 90 documents are given in Latin language and 40 out of 90 documents are given in modern Italian language. Documents count from 400 to 6000 characters each. 40 out of 50 Latin documents are extracted from Cicero's works (106 BC -43 BC), in particular from De Inventione, De Oratore, De Optimum Genere Oratorum, De Natura Deorum and De Officiis. 10 out of 50 Latin documents are extracted from Virgil's Aenead (70 BC -19 BC). The documents from the two different authors belong to a different historical period and the writing style of the two authors is also different.
Results and Discussion Next, we demonstrate the efficacy of our framework as a combination of feature representation and clustering method, in correctly discriminating between Latin and modern Italian documents. Specifically, we show in Table  1  the clustering results obtained from our framework (named as GA-ICDA + ) on the custom oriented document database and compare them with the clustering results obtained from other five algorithms on the same database. They are three clustering methods, Hierarchical Clustering, K-Medians and Self-Organizing-Map (SOM), which are different well-known strategies for text document categorization  [13] ,  [15] ,  [19]. In particular, we chose to adopt K-Medians instead of K-Means because the first one uses the same L 1 norm as our method GA-ICDA + and because it is more robust to outliers than K-Means.
The other two algorithms are the GA-IC framework for image database clustering  [1]  and the GA-ICDA framework  [3] , which is the extension of GA-IC for document database clustering, without the modifications introduced for GA-ICDA +. All the algorithms, K-Medians, hierarchical clustering, SOM, GA-IC and GA-ICDA adopt the same run-length feature vector representation used from GA-ICDA +. Clustering results are showed in terms of five methods for performance evaluation: precision, recall and f-measure indexes  [2] ,  [12] , purity, entropy, Normalized Mutual Information (NMI)  [2] ,  [17] ,  [18]  and Adjusted Rand Index (ARI)  [14]. Precision, recall and f-measure are reported separately for each language class (Latin and modern Italian) in correspondence to each algorithm. For the other performance measures, purity, entropy, NMI and ARI, a single overall value is reported for each algorithm.
Purity, entropy, NMI and ARI are well-known performance measures for clustering evaluation. On the contrary, the computation of precision, recall and f-measure requires that the correspondence between each cluster detected from the algorithm and the true language class is known. Consequently, we associate each cluster with the true language class whose corresponding number of documents is in majority in that cluster. The number of clusters nc found from the algorithms is also reported. A trial and error procedure has been adopted on benchmark documents, different from the documents in the considered database, for tuning the algorithms parameters. The parameter values providing the best possible results on the benchmark documents have been adopted for clustering the custom oriented document database.
5. The algorithms have been implemented in MATLAB R2012a. Experiments have been run on a Desktop computer quad core 2. 3GHz 4GB RAM and Windows 7. Each algorithm has been executed 100 times and the average values of each performance measure together with the standard deviation values (in parenthesis) have been reported. Our framework takes 55 s for each execution on the database of 90 documents. We observe that our framework, which is the combination of run-length features and GA-ICDA + clustering method, performs successfully, overcoming all the other clustering methods (see Table  1 ). In fact, GA-ICDA + obtains the perfect distinction between Latin and modern Italian documents, with a number of clusters equal to 2, precision, recall and f-measure values of 1. 00 for both Latin and modern Italian language classes, purity, NMI and ARI values of 1.
Conclusions The paper introduced a new framework for the discrimination between documents written in Latin and modern Italian languages. It is characterized by the position of each script letter in the baseline, derived by its energy profile, for mapping into uniformly coded text. The statistical analysis of the coded text, represented as an image, is performed by the run-length matrix calculation for texture feature extraction. The obtained feature vectors revealed satisfactory dissimilarity of the documents in different languages. Such a dissimilarity is the basis for successfully document clustering by the extension of a state-of-the-art classification tool GA-ICDA +. Experimental results demonstrated the superiority of the new framework with respect to the other clustering methods. Future work will extend the experiment to larger databases and multiple types of language feature representations.
A majority of these posts are multilingual in nature and many of them involve code mixing. The multilingual aspect of social media content is reflected in the use of multilingual words as well as in the writing script. For the ease of use multilingual users often pose questions in non-native script. Focusing on this current multilingual scenario, code-mixed cross-script (i. e. , non-native script) data give rise to a new problem and present serious challenges to automatic QA. In the work presented in this paper, Bengali is considered as the native language while English is considered to be the non-native language. However, the dataset construction approach presented in this paper is generic in nature and could be used for any other language pair. Apart from introducing this novel problem, this paper highlights corpus development process and a suitable evaluation framework.
Corpus Acquisition Because of the following characteristics of social media, we consider social media content for code-mixing cross-script QA corpus: i) Substantial and ever increasing user base. ii) A sizable volume of informal text data are added on various domains on a daily basis. iii) Various APIs are available to access social media data. iv) Most likely source of getting code-mixed data. Even though acquiring a sizable volume of the code-mixed cross-script data is not a tough task, our work on developing a QA system for code-mixed crossscript data is at its initial stages. Therefore, we have collected a small set of data which could be increased in future following with a similar approach. Research in QA system primarily requires three data resources: (i) question which is asked to get a piece of information, (ii) answer to an asked question as a response, and (iii) potential sources of the answers from which a QA system can directly or indirectly infer an answer to a question.
Annotation For document management and storing, EXtensible Markup Language ( XML) was chosen because of its popularity and ease of understanding. The QA annotation framework which was adopted in this work is depicted in Fig. 1. The tagset defined in Table  1  was used for three purposes: document information, message annotation and QA annotation. We will format the corpus in Text Encoding Initiative 5  (TEI) in future. A document in the corpus comprises of data section and question section. The data section contains the public posts collected from social media. Each public post is referred to as a message and described in the < msg > tag. Each message is assigned a unique number, i. e. , msg Id. The factoid questions follow the data section. Each question is marked by the Q tag, (i. e. , < Q > and < /Q >).
Acknowledgements This work has been partially supported by a Ramón y Cajal research contract ( RYC-2014-16935 ) and the research project  APLE 2  ( FFI2009-12188-C05-01 ) of the  Institute for Applied Linguistics (IULA). Acknowledgments. This work was partially supported by the Grant of the  Ministry of Education, Science and Technological Development of the Republic Serbia , as a part of the project  TR33037. Authors are fully grateful to  Ms. Zagorka Brodić , professor of  French and Serbo-Croatian languages , for the helpful discussions about Latin and Italian languages.
Conclusions In this paper we presented a novel research problem -cross-script code-mixed QA. Our major contributions include (i) proposing an annotation scheme, ii) creating a dataset which is the first resource of its kind, and (iii) proposing an evaluation strategy that is suitable to our corpus annotation. Bearing in mind the small dataset, the proposed evaluation methodology and created dataset will be helpful for the QA research and development community, particularly those who want to address code-mixed cross-script QA.
We adapted the pedigree analysis method implemented in PMx to analyze pedigrees (records of descendant lineages) of group-living species. In addition, we developed a group pedigree data entry sheet and group2PMx, a converter program that enables group datasets to be imported into PMx. We show how pedigree analysis of a group-living species can be used for population management using the studbook of the endangered Texas blind cave salamander Eurycea rathbuni. Such analyses of the pedigree of groups can improve the management of group-living species in ex situ breeding programs. Firstly, it enables better management decisions based on more accurate genetic measures between groups, allowing for greater control of inbreeding. Secondly, it can improve the conditions in which group-living species are held by adapting husbandry practices to better reflect conditions of these species living in the wild.
2012 ; available at www. vortex10. org/PMx. aspx) is a program used to analyze pedigree data and perform genetic and demographic analyses. Zoo and aquarium communities manage ex situ populations using PMx with SPARKS and PopLink, two record-keeping programs used to store pedigrees  (Faust et al. 2012; ISIS, 2012). PMx can analyze pedigrees which contain uncertainty of parentage  (Lacy 2012) , and this can, in theory, be extended to analysis of group pedigree data  (Traylor-Holzer 2011). However, such analysis is currently limited in practice, as SPARKS and PopLink cannot handle data of group-living species. For this reason, there are currently very few pedigrees (i. e. , studbooks) kept for group-living species and it is thus uncommon for most of these species to be genetically managed. A rare example is the Texas blind cave salamander Eurycea rathbuni, a cave-dwelling amphibian from San Marcos, south-central Texas  (Hammerson and Chippindale 2004 ).
In this paper, we address the challenges of group management  (Leus et al. 2011 ) by extending pedigree analysis techniques from individual-managed diploid species to the genetic management of group-living diploid species. Herein, we (1) explain the terminology and genetic measures used in PMx for pedigree analysis of group-living species, and (2) describe a new Group Management Package, which includes a user-friendly record-keeping template for recording pedigrees of group-living organisms, and group2PMx, an executable program that converts group datasets to be readable by PMx. We provide an example of pedigree analysis of a group-living species, the Texas blind cave salamander, and show how this can be used for the management of the population. Our aim was to provide a practical tool for the genetic management of group-living species and encourage the use of this procedure among conservation managers and institutions involved in ex situ conservation programs.
Excel spreadsheet template The spreadsheet has been developed as a user-friendly Excel file which contains the information required to analyze pedigree data for groups in PMx. This spreadsheet contains three different sections for the required information: group characteristics, parentage, and transfers between groups (moves). Although the main purpose of the spreadsheet is recording of group information, it is also possible to keep the records of individuals as a substitute for the more extensive studbook records in SPARKS. group2PMx To import the group data into PMx from the Excel template, we have developed group2PMx, a Python-based executable program. group2PMx converts the data from an Excel spreadsheet template into acsv file compatible with PMx. Detailed information about the spreadsheet template and group2PMx can be found in the manual, which can be downloaded together with the Group Management Package from: www.
An example of pedigree analysis for groups To demonstrate the application of pedigree analysis to group-living organisms, the above tools and methods were applied to the studbook of the ex situ population of the Texas blind cave salamander (Appendix S1), held by seven institutions in the USA. We first translated the studbook into a group pedigree according to the group management terminology used by PMx, and stored it in the Excel spreadsheet template from the Group Management Package (Appendix S2). A visual representation of the pedigree analyzed is given in Figure  1A. The transformed pedigree spreadsheet was then converted using group2PMx and imported into PMx. The previously presented genetic estimators (f xy , F x , MK x , GD) were calculated using PMx. The wild-caught founders for this ex situ population were not included in the calculations of the population statistics, MK x and GD, as recommended by  Lacy (1995).
Discussion In this paper, we extended pedigree analysis to managing group-living organisms and illustrated such analysis on the pedigree of the Texas blind cave salamander. As in traditional pedigrees containing information at an individual level, ex situ breeding programs of groups would benefit from recording information about the group size at each generation of reproduction and its parental group(s). To our knowledge, there is hardly any pedigree information kept for group-living species, due to the difficulty in recording and the lack of tools and guidelines to store datasets. Once this information about the groups has been recorded at each generation, the kinship of a group to all other groups can be estimated through the subsequent generations and optimal breeding decisions and management recommendations can be provided.
Acknowledgments The authors would like to thank  Sean Chester  for helping with the development of group2PMx, and  James H. Williams  and  Paula Tataru  for useful discussions and suggestions in the manuscript. We are grateful to the  Association of Zoos and Aquariums (AZA)  for providing the studbook of the Texas blind cave salamander, and to  David Heckard  for the photograph in Figure 1C. We also thank two anonymous referees for their comments. BJM benefited from a joint grant from the  European Commission  and  INRA Animal Genetics , within the framework of the  Erasmus-Mundus joint doctorate "EGS-ABG ".
We can observe a tendency towards a growth in the internationational trade up to the year 2008, an abrupt devresement for 2009 and then the struggling to surpass the effects of recession. Although it can be argued that the GUAM`s FTA and other trade agreements as well as the involvement of the Word Bank and the International Monetary Fund had also an impact in the recovery of the trade flows, the data show that in terms of trade these states had followed the international market trends. As far as tariffs are concerned, GUAM's FTA is more ambitious than the one signed by the Community of Independent States, as it does not include exceptions to free trade. On the contrary, it emphasizes that other obstacles to the free movement of goods and services should also be removed. Interestingly, the contracting parties stated that they would establish the harmonization of their legislation so that they could ensure the proper and efficient functioning of free trade.
INTRODUCTION Wind tunnel testing has been extensively used in the development and improvement of rotorcraft designs in addition to providing databases for refinement of theoretical models. The Sikorsky UH-60 helicopter is one of the more thoroughly tested rotorcraft systems, having undergone extensive flight and model-scale wind tunnel testing. One of those testing has included hover and forward flight performance tests conducted by the U. S. Army Aviation Engineering Flight Activity (AEFA) at Edwards AFB and tests of a highlyinstrumented rotor 2  at the U. S. Army Aeroflightdynamics Directorate and NASA Ames Research Center. To expand the existing UH-60 database and to investigate rotor performance and loads in the low speed flight regime, a full-scale UH-60 rotor test has been conducted in the 80 x 120m wind tunnel.
SOFTWARE ANALYSIS 3. 1 Software description QBlade is an open source wind turbine simulation and calculation software hosted by sourceforge. net. The integration of the XFOIL/XFLR5 functionality allows the user to design airfoils and analyze them in 2D and 3D (lifting surface)  5, 6. The software is adequate for teaching, as it provides an easy way to simulate a model wind turbine. QBlade also provides processing functionality for the rotor and turbine. In addition to that, the software is a very flexible and user-friendly platform for wind turbine blade design that can be used to simulate rotary wings for educational purposes. XLFR5 is an airfoil design and analysis program, the most "user-friendly" of its type. This software uses the vortex panel method and integral boundary layer equations to calculate airfoil pitching moment at different angles of attack, drag and lift.