dataesr/scientific-paragraphs-categorization huggingface dataset:
Creative Commons License All of the materials and resources used to construct the Brazilian Portuguese Lexicon were free, with open access of the NILC from the Linguateca  [17, 38]  and R software and packages doi:10. 1371/journal. pone. 0144016. g003 [39] [40] [41] [42]. After constructing the database and creating the internet page interface with the specific domain, we registered the Brazilian Portuguese Lexicon with a Creative Commons License <https://creativecommons. org/> of the Attribution-Non Commercial-ShareAlike 4. 0 International type <http://creativecommons. org/licenses/by-nc-sa/4. 0/>, which attributes to authors the appropriate credits, does not allow the use of the material for commercial purposes, and allows users to share, copy and redistribute the original material and to adapt, transform, and distribute the material under the same license as the original.
Lexicon distribution With the Brazilian Portuguese Lexicon database and internet site ready for use, we analyzed its general distributions and the interactions between variables (i. e. , informational columns) to provide an overall description and a global picture of the Brazilian Portuguese Lexicon. In Fig 4 , we present the main general distributions; and in Fig 5 , we present the variable interactions between grammatical categories, number of letters, orthographic neighborhood, OLD20, and number of words  [2, 53]. For a clear analysis, we applied basic filters to the full Brazilian Portuguese Lexicon data to eliminate extreme outliers and specific word information errors. We removed words with: a) more than 20 letters (. 25%), b) more than 40 orthographic neighbors (. 37%), c) more than 14 OLD20 (.
01%), and d) more than 5 homographs (. 03%), accounting for a total of. 62% of the entire database. The general means and standard deviations according to grammatical category are shown in Table  5. Moving to the general distributions, Fig 4A shows that as expected, nouns and verbs are clearly the main categories, accounting for nearly 80% of the corpus, followed by adjectives, which account for less than half the number of nouns and verbs. Adverbs, grammatical and numerals represent only 1. 79% of the corpus. Obviously, this asymmetric distribution is determined by the former's open and latter's closed grammatical categories and by the derivational and inflectional productivity within these categories  [21, 43, 44]. Because verbs have a large inflectional paradigm in Portuguese, they account for the largest number of word types, whereas nouns are the main lexical category, comprising the highest number of lemmas.
It is interesting to note that corpora based on subtitles have provided amazing results as frequency predictors on lexical processing, especially in lexical decision and word naming tasks  [1, [11] [12] [13] [14] 25, 26, 55]. Also, subtitle and internet corpora are extremely interesting for language evolution, neologisms, textual genres, typing and spelling errors, and informal language analysis. Nevertheless, according to Cai and Brysbaert (2010, p. 4)  [14]  "criticisms can be raised against films as a representative source of language (they often depict American situations, are biased towards certain topics such as police investigations, do not include everything that is said, the language is not completely spontaneous, etc. )". Indeed, subtitle corpora presents mostly dialogue genre, oral modality words, and are adapted in translated films, which could be considered a phonological database, while written corpora could be considered an orthographic database, that is, the words used in written modality, as differentiated in the Lexique  [4].
Therefore, the Brazilian Portuguese Lexicon corpus presents useful characteristics in terms of its size, accessibility, speed of use, and information about the Brazilian Portuguese lexicon. The sections above described the general features of the Brazilian Portuguese Lexicon, analyzed the general distributions and interactions of different variables, and compared to the other current Brazilian Portuguese databases SUBTLEX-PT-BR  [15]  and Worldlex (Portuguese Brazil)  [16] , confirming that most characteristics of the Brazilian Portuguese Lexicon are in perfect harmony with the other useful psycholinguistic word-based corpora  [2] [3] [4] , and is superior than the current Brazilian Portuguese databases SUBTLEX-PT-BR  [15]  and Worldlex (Portuguese Brazil)  [16]  when regarding a) number of word types, b) number of metalinguistic and psycholinguistic information provided, c) Pearson correlation to other corpora, d) POS-tagged information, e) control of foreign language words, f) control of proper names, g) internet site and interface facilities, h) downloads available, i) corpus origin, j) pseudoword generation engine, k) statistical linguistic tools, and l) free and open-code access.
Final Remarks and Developments In this article, we presented the psycholinguistic word-based corpus: Brazilian Portuguese Lexicon, Léxico do Português Brasileiro (LexPorBR). It is a stable, reliable, and complete database that we strongly believe will have long-term utility, meeting all needs for psycholinguistic, computational linguistic, and language modeling research in Brazilian Portuguese, as also multilingualism and cross-linguistic research  [2, 28]. The Brazilian Portuguese Lexicon was constructed from the NILC  [38] ; it contains more than 30 million word tokens, 215 thousand word types, 160 thousand lemmas, 290 thousand proper names, and 25 columns of information; it is accessible free of charge on the internet; and it provides a basic pseudoword generation engine  [53] , as also a statistical module in the results  [18].
The Brazilian Portuguese Lexicon is also available for download in the S1 File. We called this first version of the Brazilian Portuguese Lexicon the Alpha version; the Beta version will expand on the available information and will contain phonological and syllabic forms and, consequently, all of the derived phonological and syllabic categories  [2, 4, 19, 56, 57] , providing more information and possibilities in the corpus searches. Indeed, the Brazilian Portuguese Lexicon words will be compared with Brazilian Portuguese dictionaries to develop more selective and restrictive criteria about existing words, grammatical categories, grammatical and semantic information. Finally, the Brazilian Portuguese Lexicon has a basic Brazilian Portuguese pseudoword generation engine that creates pseudowords based on bigram and trigram structure and frequency as a function of the grammatical categories, and we are working with different types of pseudoword engines based on our corpus data  [53, 58, 59 ].
Using the macro Calmar, developed in SAS (SAS Institute, Cary, NC, USA) by the French National Institute of Statistics and Economic Studies (INSEE), the data from this network were extrapolated to the regional population covered by the occupational medicine (excluding some special social systems not represented in the study) to determine the extent of the phenomenon at the regional level  (22). Thus, we can estimate that >268 000 employees reported backache during the preceding seven days and more than half a million (316 552 men and 244 253 women) during the 12 months preceding the survey (ie, 59. 3% of men and 55. 0% of women). Considering that 2-7% of people with non-specific LBP are at risk of developing chronic pain and disability in due course  (23) , this would indicate 11 216-39 257 employees at risk of developing severe and durable work disability.
, 2010a,b)  and determine whether the effect of location may extend across multiple races of perceivers. Given that the effects of spacing and size on the key facial features were investigated separately in the current study, the question of whether these effects might interact also remains open. Future studies might therefore vary spacing and size at the same time to test the effect of the interaction between these two factors. In summary, we examined the sensitivity of observers to configural and featural changes in the upper and lower regions of own-and other-race faces. Our data reveal that the ORE is more pronounced when featural and configural changes are in the upper region than in the lower region. These findings indicate that information from the upper vs. lower region of the face contributes differentially to the ORE in face processing, and that processing of face race is influenced more by information location (i.
Acknowledgments This research was supported by grants from the  Natural Science and Engineering Research Council of Canada, National Institutes of Health  ( R01 HD046526 ),  National Science Foundation of China  ( 31100737  and  31371032 ),  Zhejiang Provincial Natural Science Foundation of China  ( LY13C090005 ) and the  Research Foundation of Educational Department of Zhejiang Province of China  (No. Z201122112 ).
Indeed we support a design approach based on models, so-called Model-Driven Engineering (MDE). In the MDE software process methodology, refining models develops a system. The model's transformation technique is a key principle within this approach. It allows traceability between the different models produced at the different levels of abstraction. In TEL systems, most of the problems reported in the literature  [2]  show that training supported by ICT is often the simple transposition of face-to-face situations where teachers provide the learners with their knowledge (behaviorist pedagogy). This kind of pedagogy tends to place learners in a rather passive situation and therefore does not encourage them to participate actively during the course. On the other hand, we think that proposing a tool to support learning situations is not sufficient, and therefore it is necessary to define a coherent and effective learning method.
Experimental setup Participants sat at an experimental table with their forearms resting on the table (see Figure  1 ), so that the right index finger was lying comfortably on the force transducer (SCAIME, ZFA, 50 kg). To collect the force data and to provide visual feedback to the participants, a customized LabView (National Instruments) program and a National Instruments acquisition card (DAQ NI-USB 6008-National Instruments) were used. The force data were sampled at 240 Hz and saved for later analysis. In front of the participants, the target area and task-specific online feedback was presented on a 19" screen. The online feedback consisted of a bar indicating the level of the produced force in% MVC in reference to the target level (see Figure  1 ). It was given at a sampling rate of 12 Hz (i.
25 cm per 1 N. For the maintenance task, two lateral red bars represented the target line (i. e. , the force level in % MVC to reach) and a mobile blue bar restituted the applied force level. remained illuminated until the trial was completed. The participants were instructed to release their force when the bar turned off. The start of each trial was self-paced, but a mandatory break of 3 s was implemented in the program. Data were stored in absolute force values.
Tasks and procedure Participants were informed verbally about the general procedure and were provided with written instructions explaining the task and their positioning during the trials. The experimental setup is summarized in Figure  1. Firstly, MVC was measured by using data from three trials of 5 s each. This procedure is the standard procedure to determine MVC for isometric force production with the finger  [15, 21, 26, 48, 49]. The participants were instructed to press as hard as they could on the force transducer to lift then hold the mobile bar on the screen as high as possible. A resting period of about 30 s was given between trials. The mean force obtained during the last three seconds of each trial was considered as the MVC and was used to calculate the relative target force levels of the consecutive task conditions.
Data analysis The acquired data of the produced force over time were analyzed with the use of a Matlab R2012b customized program (MathWorks, Natick, MA, USA). The data were low-pass filtered with a 4th order Butterworth filter at 30 Hz. To exclude the ramp phase from further analysis, the first 2. 5 s were discarded. All the following analyses were accordingly conducted on 12. 5 s trials. Data were converted to relative force values in percentage of the participants' individual MVC. All variables were calculated per trial and then averaged over the two trials. The mean of the produced force, the standard deviation, and the CV were calculated to characterize general properties of force production CV = SD/mean force. As an index of efficiency, the signal-to-noise ratio was computed as: Signal-to-noise ratio = mean force/SD.
Namely, three functionally relevant scales were selected: Scale 4 (16 ms), representative of the signals' frequency range after filtering (i. e. , 0-30 Hz); Scale 10 (41 ms), more representative of the mechanisms known to operate in force control, that is, sensorimotor processing and physiological tremor (0-12 Hz); Scale 30 (125 ms), the most representative of sensorimotor processing (0-4 Hz). The structure of the force output in the frequency domain was characterized by spectral analysis. The power spectrum was calculated by use of the pwelch function implemented in Matlab by means of a Hanning window with 256 data points with a non-overlapping window of 1024 data points. To characterize the overall changes in the distribution of power, the slope of the log-log plot was determined per participant for each condition.
Data acquisition and analysis The EEG, EMG and breathing of mice unrestrained except for the electrode tether were recorded continuously inside a modified 2-chamber whole-body plethysmograph (PLY4223, Buxco, Wilmington, NC, USA)  [34]. The other recordings were performed on freely-behaving mice housed individually in cages with simultaneous acquisition of the EEG, EMG, and arterial pressure  [32]. Data analysis was performed with MatLab (Mathworks, Natick, MA, USA). Scoring of wakefulness, non-rapid-eye-movement sleep (N), and R was performed visually by scrolling through raw EEG and EMG recordings at 4-s resolution  [32]. The minimal duration of wakefulness, N, and R episodes was set at 12 s in sleep-wake structure analysis  [29]. R episodes at sleep onset representing cataplexy-like events (CLE) in rodents were scored following consensus criteria  [35].
Statistics Statistical tests were performed with SPSS (SPSS, Chicago, IL, USA) and significance at P < 0. 05. Data are reported as means ± SEM. Differences in the distributions of the wakefulness episode duration were analyzed by Kolmogorov-Smirnov test. The other analyses were performed with ANOVA (GLM procedure with Huynh-Feldt correction when appropriate). Between-group differences were tested with t-tests only in case of significance of the main effect of group at ANOVA in order to limit type I errors.
During interviews, the researcher opportunistically came up with "why" questions on the basis of participants' statements. In addition, one closing question was asked to participants: "What is the worth of Cocoon from your point of you?" We conducted 19 semi-structured interviews. Considering our large design space, a wide range of possible users participated to the study: people ranging from 21 to 63 years old (mean: 30), from different social classes and with different professions. Interviews lasted about one hour and were supported by storyboards illustrating the identified features. During interviews, storyboards were first presented to participants. Then, they were questioned in order to gather their comments and feedback regarding Cocoon. Interviews were recorded and transcribed. Data analysis was carried out by two researchers (the researcher who conducted interviews and another one) and followed 3 steps: (1) extraction of statements related to both positive and negative sides of the system, (2) translation of the extracted statements into well-defined worth elements and association of relevant verbatims with them (worth enrichment), and (3) classification of worth elements into 'universes' of worth.
With this renaming, PEW becomes the ARROW framework (Appreciations, Requirements and Rationale Of Worth). Outcomes from our literature review on WMs also revealed a lack of appropriate tools for WMs construction. Indeed, for instance, during our review of the WCDrelated literature, we discovered LadderUX 1  , an online tool supporting quantitative data analysis from laddering. However, even though it represents an interesting tool, LadderUX is not well-suited for WMs construction since it only considers classes of elements specific to MECs (attributes, consequences, and values). Yet, a major difference between HVMs and WMs lies in the refinement and extension of considered classes of elements  [10]. In the absence of an appropriate tool, we proceeded to constructing Cocoon's WMs using Microsoft PowerPoint.
It is worth noting that WMs construction was also successfully achieved using Microsoft Visio  [10]. Figure  3  depicts examples of excerpts of WMs constructed during the development of Cocoon at respectively design and evaluation phases and on the basis of our ARROW framework for worth mapping. Activities carried out during the development of Cocoon in design phase also include the creation of mock-ups using the Axure software 2. Since Axure allows mapping desired behavior to UIs, both the system's UI and associated interactions were illustrated through several mock-ups. It is important to note that the first WM of Cocoon, constructed at the end of the study of needs phase, supported the creation of mock-ups particularly regarding the UI design. After the mock-ups creation completed, usability testing (an evaluation activity) was conducted.
Evaluation In WCD, the system evaluation should start at an early stage. As related earlier, in the development of Cocoon, evaluation started at the end of the study of needs phase with worth translation (see section 3. 2). The evaluation process continued throughout the system development and usability testing was conducted at design phase (see section 3. 3). Here, we focus on the final evaluation of the system implemented on the basis of mock-ups resulting from design phase. It is important to note that the final version of the Cocoon application was instrumented with a probe collecting actual usage data (received information, sent and received posts, access to control parameters). Considering the requirements for evaluation in WCD, we elaborated a strategy consisting of a field study for experience with Cocoon in the real word, SUS questionnaires for a subjective assessment of the system's (real) usability, and group interviews for feedback.
We conducted a field study of three weeks with 15 participants ranging from 19 to 31 years old (mean: 25). In order to ensure social interactions, we recruited five groups of three people knowing each other. Most participants were friends. We only had one group of colleagues (working as air-traffic controllers), one group with a couple (partners living in the same house), and one group with two cousins. About one week after the start of field trials, we sent the SUS questionnaire  [1]  to participants. After field trials ended, we conducted interviews with the five groups of participants. All group interviews were video-recorded. Data collected during evaluation was analyzed using different techniques. Participants' discourses were transcribed and analyzed similarly as in the study of needs phase (see section 3.
2). The subjective usability score was computed from SUS questionnaires and various statistical treatments were applied to actual usage data. Outcomes from evaluation data analysis allowed us understanding and highlighting worth of Cocoon. However, a main issue remained unresolved: How to assess worth achievement in terms of the 5Ds as suggested in the WCD literature? Our review of the WCD literature also revealed the lack of a framework and/or method supporting assessment of worth achievement according to the 5Ds. In order to fulfill this need, we engaged in the development of a method for worth assessment. Giving their potential, we investigated WMs for this purpose. Indeed, WMs based on the ARROW framework are well suited for supporting worth evaluation because the framework proposes that WMs capture the system's global state by considering both perceived positive and missing elements, but clearly separated, and associations between them.
Neural investigations suggest that there are three possible core deficits in dyslexia: phonemic, grapho-phonemic, and graphemic. These investigations also suggest that the phonemic deficit resides in a different mode of speech perception which is based on allophonic (subphonemic) units rather than phonemic units. Here we review the results of remediation methods that tap into each of these core deficits, and examine how the methods that tap into the phonemic deficit might contribute to the remediation of allophonic perception. Remediation of grapho-phonemic deficiencies with a new computerized phonics training program (GraphoGame) might be able to surpass the limits of classical phonics training programs, particularly with regard to reading fluency. Remediation of visuo-graphemic deficiencies through exposure to enhanced letter spacing is also promising, although children with dyslexia continued to read more slowly than typical readers after this type of training.
However, the results of two recent meta-analyses of training studies  (McArthur et al. , 2012; Galuschka et al. , 2014)  indicate that classical phonics instruction is the only treatment approach whose efficacy in children and adolescents with reading disabilities is statistically confirmed. Furthermore, as noted by Gabrieli (  2009 ), only about 50% of dyslexics retain the reading progress they make after explicit and systematic instruction in decoding strategies and phonemic awareness, and those who do retain their gains do not attain the fluent reading competency of typical-reading children. A new computerized phonics training program (Grapho-Game) appears to be able to surpass the limits of classical phonics training programs, especially with regard to reading fluency. The aim of GraphoGame is to strengthen the binding between the orthographic and phonological encodings of words (for a review,  Richardson and Lyytinen, 2014).
The GraphoGame method is mainly based on the training of grapheme-phoneme correspondences. This method progresses from the simultaneous and repeated presentation of grapheme-phoneme correspondences (first in isolation, then included in syllables, and afterward in words) to fluency training with words and sentences. GraphoGame's effectiveness in improving reading acquisition has been demonstrated in several studies conducted in languages with various levels of orthographic transparency: Finnish (  Saine et al. , 2010 Saine et al. , , 2011)) , German  (Brem et al. , 2010) , and English  (Kyle et al. , 2013). In the Finnish study, after a screening of 166 first graders [with tests assessing letter knowledge, phonological awareness, and rapid automatic naming (RAN) of letters, digits, or pictures of frequent words], the lowest-achieving 30% were randomly assigned to two different remedial interventions (25 children in each group): a regular remedial phonics intervention (RRI; Note 2  ) or a computerized assisted intervention using GraphoGame (computer assisted regular remedial intervention, CARRI), both with four weekly sessions of 45 min for 28 weeks.