dataesr/scientific-paragraphs-categorization huggingface dataset:
The Learning Designer The Learning Designer 3  (Laurillard et al. 2013 ) is a community knowledge-building tool that supports teachers in creating, modifying, finding, sharing and reusing learning designs. Its aim is to support the teaching community in collaborating on how to design and express its pedagogical ideas, especially in relation to the use of technology-enhanced learning to improve learners' experience. The software environment represents learning designs using formal learning concepts (e. g. Bloom's taxonomy of learning outcomes and a typology of forms of learning), underpinned by semantic technologies. This kind of computerinterpretable representation enables the tool to provide visual analytics of the learning designs (for example, in the form of pie charts representing the character of the designed learning experience) and intelligent recommendations using community knowledge.
The resulting OpenGLM model can be shared with other designers using OpenGLM for viewing, editing and exporting to IMS-LD. The exported IMS-LD package is a valuable artefact that enables the underlying learning design to be reused through being shared with other IMS-LD authoring tools and deployed in IMS-LD runtime environments  (Derntl, Neumann, & Oberhuemer 2011). However, the fact that the ''healthy eating'' lesson was to take place primarily in a face-to-face setting led to certain peculiarities during modelling, as IMS-LD was conceived primarily for the deployment of computer-managed learning opportunities  (Derntl et al. 2012). In general, it is difficult to model non-software resources and services in IMS-LD, such as the ''device'' described in the lesson plan (which can be represented by an environment of services and learning objects) and other physical artefacts (e.
Within each pattern or phase, the tool can also model the number of groups and the students' flow across the different groups (Figure  7B ), as well as the assessment plan, which here includes a quiz with subsequent feedback from the teacher (Figure  7A ). However, Web Collage's representation of the lesson also has limitations. There is no way to specify the duration of an activity, and there is only limited provision for modelling resources. For example, although the teacher-designer can define the URL of a certain Web-based resource, or specify the use of an interactive whiteboard, they cannot describe how the resource will be used, or how to access the whiteboard system. Another limitation is that, in activities other than assessment, Web Collage models the kind of task to be performed using natural language only in the description of each activity.
g. ''Collect my evidence''). Each of these eight activities was modelled through one or more interactional situations (e. g. ''Collect data from the food diary meal'' and ''Organise data collected''). Figure  9  shows the ScenEdit screen on which the interactional situations are described. In terms of tools, each student needs a laptop, a camera and an Internet connection in order to access the nQuire project. In terms of resources, the nQuire website contains the data, either collected by students or provided by the teacher (diagrams, notes, description of objectives, links and so forth). Five roles were Figure  8. The ''healthy eating'' lesson modelled using ScenEdit. identified (pupil, group, class, teacher and nutritional expert), as well as a number of locations (including home and restaurant).
Thus, the transformations described in this paper are merely examples of the kinds of transformations which a teacher's pedagogical ideas may undergo when they are implemented in digital form (for a further discussion on this topic, see Mun ˜oz-Cristobal et al. 2012). Throughout the preceding five sections of this article, we have noted the difficulties encountered when modelling the ''healthy eating'' scenario in the authoring tools. These difficulties, which we have synthesised in Table  1 , were normally associated with face-to-face activities (e. g. classroom discussions) and physical resources (e. g. interactive whiteboards), and were more prominent in tools compliant with the IMS-LD standard (i. e. OpenGLM, CADMOS and Web Collage). When using these tools to model the ''healthy eating'' scenario, the research teams often had to circumvent their limitations by modelling activity and resource types in slightly counter-intuitive ways, in contrast to the tools that rely more on natural language descriptions and teachingÁlearning activity types derived from teaching practice (e.
CORPUS FOR THE LISTENING TASK A few weeks before the experiment, the readers were recorded while reading 20 short texts (fables from Aesop, La Fontaine, and Lessing). In a few cases some words of the original text were changed, since these words are not used any more and sounded antiquated. Readers were instructed to first read all the texts with a normal volume level (N condition) and then with a loud volume level (L condition). This order was chosen to avoid carryover influences from L to N. The acoustics and the breathing movements produced during reading were recorded synchronously, as described in Section "Data Acquisition, Processing, and Labeling. " The acoustical signals were processed afterward to select a subset of texts for the listening task. The mean difference in intensity from N to L was computed for each text and each reader using Praat  (Boersma and Weenink, 2011).
Recording of acoustic and breathing signals The acoustic signals were recorded by means of a super cardio condenser microphone (Sennheiser HKH50 P48) coupled with a pre-amplifier. The movement of the rib cage and the abdomen were monitored using an Inductance Plethysmograph (Inductotrace, formerly Respitrace TM ). The acoustic and the breathing signals were recorded synchronously by means of a six channels voltage data acquisition system (DATaREc ® 4DIC6B/DIC6L) and a computer program that sampled acoustic and breathing signals at 11030 Hz. Simultaneous recordings of acoustic and breathing signals were collected during the listening task. This allowed the output of the loudspeakers to be acquired together with listener breathing. One band of the Inductotrace system was positioned at the level of the axilla (rib cage) and the other band at the level of the umbilicus (abdomen; see Figure  1A ).
CALCULATED PARAMETERS AND DATA SELECTION From the labeling described above, we computed the amplitude (in %MD), and the duration of the breathing cycle (Figure  1C ). The amplitude (ampI) was the displacement from the onset to the offset of inhalation. The duration was the time delay from the onset of one inhalation to the onset of the next inhalation (durC). For each listener, breathing cycles with ampI and durC at more than 1. 5 the inter-quartile range from the first and third quartiles were excluded from the analyses. On average this excluded ∼10% (∼ ±1% standard error) of the breathing cycles during listening in the three experiments. These outliers could correspond to sighs or noises due to body movements. Finally, we investigated the coordination between reader and listener breathing cycles.
STATISTICAL ANALYSES We used Linear Mixed Model (lme4 package in R, version 2. 15. 2) for statistical analyses. We treated Condition, Order, and Reader as fixed factors and Listener and Text as random factors. The p-values were calculated using the method of Monte Carlo sampling by Markov chain (pMCMC = Monte Carlo Markov chain; see  Baayen, 2008). The alpha was set to pMCMC < 0. 05. Linear mixed models are reliable when the residuals of the model are normally distributed. For each model, the linearity of the residuals was checked using diagnostic tools. When the residuals were not linearly distributed, which was the case for the analyses of the duration of the breathing cycles in Experiments 2 and 3, the values of all subjects and conditions were transformed to a logarithmic scale  (Baayen, 2008).
MAIN QUESTIONS The data were analyzed with respect to the following main questions: (A) Are listener breathing patterns affected by the Reader (Experiment 1 only)? (B) Are listener breathing patterns sensitive to the Condition and are these effects reflected in the changes observed for the readers? (C) How do listener breathing kinematics adapt to the listening task over time, i. e. , from one listening trial to another? (D) Do listener breathing patterns temporally align with the reader breathing? We did not have any specific hypothesis about the Order effect.
Figures  4A, B  show the mean values of the two parameters in the different trials split by Reader. We evaluated the adaptation process in the first condition by comparing trial 1 vs. 5; the change from one condition to the other, trial 5 vs. 6; and the adaptation process over the second condition by comparing trial 6 vs. 10. For the statistical analyses, data were split according to Reader and Order. Trial (with levels 1, 5, 6, 10) was taken as fixed factors and Listener as a random factor. When listening to the male reader in the NL order, durC was stable for the N condition (trials 1-5), did not significantly change when condition changed (trials 5-6) but tended to decrease over the L condition (6:10, t = -2. 1, pMCMC = 0. 036). ampI was decreasing from trial 1 to 5 but the difference was not significant.
ACKNOWLEDGMENTS This work has been supported by a grant from the  German-French University  to the  PILIOS  project and the  Ministry for Education and Research (01UG0711)  to the  Laboratory Phonology group at ZAS. We like to thank  Jörg Dreyer  for his technical expertise,  Anna Sapronova  for acoustic data analyses,  Uwe Reichel  for his support in automatic syllable counting, the two reviewers and  Blake Rodgers  and  Mark Tiede  for help in proof reading.
Repetition suppression is associated with response priming and is used as a metric to examine the processing characteristic of neuronal populations  [20] -  [22]. Recent data  [23]  and theory  [24]  suggest that RS reflects a combination of attention and predictive mechanisms (predictive coding) integrating top-down expectations with bottom-up sensory input  [25]. While a number of theoretical models have been proposed to explain RS  [20] ,  [22] ,  [26] -  [27]  all are associated with increased processing and information encoding efficiencies related to repeated stimulus properties. Here we were interested to what extent within and across-modal activation to passive vowel processing would result in RS effects in the CB. Two groups of ten congenitally blind and ten sighted adults participated in a functional magnetic resonance imaging (fMRI) study.
Stimuli The stimuli were multiple /i/ and /y/ French vowels recorded from a native French Canadian male speaker in a soundattenuated room. Multiple utterances of /i/ and /y/ French vowels were individually recorded. Seven clearly articulated tokens of each vowel were selected and digitized at a sampling rate of 44. 1 kHz with 16-bit quantization recording. Using Praat software (Institute of Phonetic Sciences, University of Amsterdam, NL), the fundamental frequency (F 0 ), and first, second, and third formant frequencies (F 1 , F 2 , F 3 ) values were calculated for each vowel from a section of the vowel located at 625 ms of the maximum peak intensity. For the /i/ vowels, the mean F 0 , F 1 , F 2 , F 3 , peak intensity and duration values were 155 Hz (68), 299 Hz (69), 2287 Hz (656), 3166 Hz (635), 70 dB (62) and 357 ms (649), respectively.
Data acquisition Magnetic resonance images were acquired with a 1. 5T wholebody MRI scanner (Siemens Sonata MR scanner) and standard headcoil in the Brain Imaging Centre (BIC) at the Montreal Neurological Institute. Auditory stimuli were amplified (Rolls RA53b Headphone Amplifier) and presented to participants with MRI compatible insert earphones (Sensimetrics S14) at a comfortable sound pressure level. Functional images were obtained using a T2*-weighted, echoplanar imaging (EPI) sequence with whole-brain coverage (TR = 10 s, acquisition time = 3000 ms, TE = 51 ms, flip angle = 90u). Each functional scan comprised thirty-five axial slices parallel to the anteroposterior commissural plane acquired in interleaved order (64664 matrix; field of view: 256 mm 2 ; 464 mm 2 in plane resolution with a slice thickness of 4 mm without gap).
Data analyses Data were analysed using the SPM5 software package (Wellcome Department of Imaging Neuroscience, Institute of Neurology, London, UK) running on Matlab (Mathworks, Natick, MA, USA). The maximum activation peaks for each cluster were labelled according to probabilistic cytoarchitectonic maps in the SPM Anatomy toolbox  [30]. If a brain region was assigned a probability less than 50% or unspecified in the toolbox, the peak coordinates were converted from MNI space to Talairach space and the brain region identified with the Talairach Daemon  [31]. The functional series was realigned for head movement. After segmentation of the T1 structural image and coregistration to the mean functional image, all functional images were spatially A General Linear Model was used to analyse BOLD activity with regressors of interest related to the seven vowel repetitions and six realignment parameters with the silent trials forming an implicit baseline.
Social isolation test. To characterize gregariousness  [20] , the audience horse was led by an experimenter outside to the barn so that it would become invisible and inaudible to the tested horse for 1. 5 min. The number of neighs was recorded. However, these data were removed from analysis, because several horses showed excessively strong signs of distress (escape tendency, etc. ) and did not complete the test. Novel area test. For this test, which characterizes fearfulness  [19] , the oor of the novel arena was divided into three zones of 2. 7 m62. 7 m (Fig. 2 ). The 1 st -3 rd zones were the start, intermediate, and arrival zones (on the right, middle, and left in Fig. 2 , respectively). The arrival zone contained a bucket of the pellets with which the horses were familiar. Immediately prior to the test, the horses underwent a habituation phase during which they learned to go from the start zone to the arrival zone containing the bucket.
Statistical analysis Data were analyzed with the XLSTAT software (Addinsoft Software, Paris, France). Because of the small number of subjects and the nature of the measurements, nonparametric statistics were used. Friedman, one-tailed Wilcoxon, and McNemar's tests were used for within-group comparisons. For acquisition, we analyzed only the sessions performed by all animals (i. e. , the first four). Because many animals performed additional sessions to reach the acquisition criterion, the figure also lists the number of responses during the last acquisition session (the one during which the horses reached the criterion). For the analyses involving temperament data, we pooled the number of responses across the first four acquisition sessions and then across the three CS presentations in the PIT session.
Acknowledgments The authors are grateful to the director ( Laurence Wimel ) and staff of the '' Station Experimentale des Haras Nationaux '' for allowing the use of the animals and facilities. We would also like to thank  Flore Pasquier  and  Elodie Gilmert  for their participation in collecting the data. We are especially grateful to  Helene Roche  for her discussion with us before we initiated this project.
Overview The flow diagram of the study is summarised in Figure  1. Thirty children were randomly selected from the Pisa HM database with the following criteria: 15 will be diagnosed with AD, and 15 will develop normally (step 1). All scenes showing a situation in which social interaction could occur (i. e. , all scenes with an infant and an adult) were extracted and, if necessary, segmented into short sequences to be scored (step 2). Raters blind to diagnosis rated parents and infant behaviours independently within each interaction sequence according to a grid with a specific part for each partner (step 3). To distinguish parents' style of affective speech according to their emotional and prosodic characteristics, we classified all parents vocalisations as parentese or other speech (i. e. , non-parentese) using an automatic algorithm  [21].
, 2010 [25] ). The children were recruited among those referred from multiple community sources to the the Scientific Institute ''IRCCS Stella Maris'', a suburban university hospital providing tertiary care to patients of all socioeconomic levels. The study has been approved by the Ethical Committee of the Stella Maris Institute/University of Pisa, Italy. The Pisa HM data base includes children from biparental families with either typical or AD matched for gender and socio-economic status (all medium or good). Each child included in the Pisa HM data base had at least one scene from each of the first 3 semesters of life showing a situation in which social interaction could occur. Also, HM run for a minimum of 10 minutes for each of the first 3 semesters of life. The control group was recruited among children attending a local kindergarten attending a local kindergarten.
It included 15 typically developing children (M/F ratio: 9/6) confirmed by non-pathological scores on the Child Behavior Check List  [26]. After the automatic algorithm affective speech classification (step 4), the experimental group included 14 children (M/F ratio: 10/4) with a diagnosis of autism disorder (AD) without any sign of regression confirmed by the ADI-R. All AD children had a Childhood Autism Rating Scale (CARS) score. 30. Mean Intellectual Quotients (SD) were 72. 86  (8. 5). Detailed clinical data for each child are given in Table  S1  (available online).
Studying parental and parentese contributions to parents infant interactions (Step 4) To extract the caregiver's vocalisations from the video segments, we performed a manual segmentation of parents' vocalisations with The Observer 4. 0H. We used Sound Forge 9. 0 to extract the vocal segments from each video sequence. Vocal segments that were presented with poor audio quality or were imperceptible to the ear were excluded. At this stage, we lost one child per group because of poor HM audio quality. Manual segmentation provided us with a database of speech segments separated by cases (AD, TD) and by semester (S1, S2 and S3). In addition, manual segmentation allowed us to identify the following: the number/frequency of vocal segments, the duration of segments in each sequence, and who was speaking to the child (i.
e. , mother, father, other CG). Detailed data regarding the number of parent, mother, and father vocalizations as well as infant responses per infant are given in Table  S2. The segments of speech were analysed using a computerised classifier for categorisation as 'parentese' or other speech'. Classification according to acoustic components was required, as the interaction database included only CG vocalisations addressed to the infant. Because the "manual" study of acoustic components of the voice takes a very long time  [27]  and only allows for the study of very short voice segments, the use of such an algorithm made it possible to conduct an extensive study of HMs based on their acoustic characteristics. The whole system was described by Mahdhaoui et al. [21]. Here, we briefly describe the key components of this automatic system.
Creation of the interaction database (step 5) We first created an interaction data base by extracting all child behaviors occurring within the 3 seconds following any caregiver behavior (including events that occur within the same second). The 3 second window was based on available literature on synchrony and previous work  [14, 15]. Extraction was performed using a Linux-based script. The sequence of n interactive patterns is termed n-gram as usually performed in natural language processing or gene analysis. In this study, we only focused on bigram modelling. Given the large number of possible types of interaction ([CG item 6 infant item]), and the low frequency of several items in the data base, we created five CG meta-behaviors (Vocal solicitation, Touching, Gestural solicitation, Regulation up, Regulation down) and six infant meta-behaviors (Behavior with object and 5 types of Behaviors with people: Vocalizations, Intersubjective behavior, Seeking people, Receptive to people, Orienting towards people,) by grouping ICBS items.
Parentese in the HM by group We first explored, as a whole, before the interactive database extraction (step 5), the proportion of parentese in parents' vocalisation and in parents' soliciting behaviour (called Regulation Up in the ICBS) by group (figure  2A ). As shown in figure  2A , we did not find any differences between groups for parents' vocalisation. The proportion of parentese was higher in mother vocalisations (40 to 60% of parentese) than father vocalisations (15 to 20% of parentese). Use of parentese among mothers showed the same decrease over time in both groups (b = 20. 51; standard error = 0. 097; p = ,10 26 ; b = 20. 55; std error = 0. 11; p = ,10 26 for S2 vs. S1, and S3 vs. S2, respectively). Parentese among fathers significantly decreased between S1 and S2 (b = 20. 4; std error = 0.
This result is consistent with ours. Third, despite previous works with another pathologic group (infants who will develop intellectual disability without symptoms of AD), we were not able to include HM from this group in the current study, as the much lower occurrence of interaction, CG vocalisation and infant meta-behaviors prevented any statistical analysis  [15]. However, to explore whether the severity of autism or comorbid ID significantly biased the current results, we performed a GLMM analysis with semester, speaker, type of speech, IQ and CARS score as the independent variables and infant response following a parental vocalization as the dependent variable. IQ and CARS scores showed no significant association with infant responses (data not shown). Finally, we modelled parent-child interactions as synchronic CGxInfant events using an approximation: CG vocalization followed by an infant response in a 3-second time window.
The Pisa "home video in autism" project was supported by grants  PRIN 2003-2005  and  PRIN 2005-2007  from the  Italian Ministry of Instruction, University , and Research. The current study was supported by a grant (nu2008005170) from the  Fondation de France  given to MC and DC, by the  UniversitePierre et Marie Curie  (Projet Emergence 2010) the  European Commission  ( FP7: Michelangelo  under grant agreement nu  288241 ), and the fund "Entreprendre pour aider". The funding agencies and the University were not involved in the study design, collection, analysis and interpretation of data, writing of the paper, or the decision to submit for publication.
Study design Data were derived from the HELENA Cross-Sectional Study (HELENA-CSS). HELENA-CSS is a multi-centre study conducted in ten European cities. A total of 3528 adolescents (age range 12?5-17?5 years) were assessed at schools between 2006 and 2007  (14). All procedures involving human participants were approved by the Ethics Committee of each city involved  (15). Written informed consent was obtained from both adolescents and their parents.
Breakfast consumption was assessed based on agreement with the statement: 'I often skip breakfast', with seven answer categories from strongly disagree (5 1) to strongly agree (5 7). Then adolescents were categorized into three groups in accordance with Hallstro ¨m et al. (12)  : (i) 'consumer' if they answered 1 or 2; (ii) 'occasional consumer' if they answered 3 to 5; and (iii) 'skipper' if they answered 6 or 7. Assessment of objectively measured and self-reported physical activity and sedentary time Physical activity and sedentary time were measured during seven consecutive days using accelerometers (Actigraph GT1M; Manufacturing Technology Inc. , Pensacola, FL, USA). Adolescents wore the accelerometers on the lower back during the waking hours. Data were saved in 15 s intervals (epochs).