dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants We recruited 58 male and 58 female participants from the student population at the University of Montpellier, France. Students were invited via the ORSEE software, from a pool of more than 4,000 volunteers, to participate in an experiment at the Laboratory of Experimental Economics in Montpellier (LEEM, University of Montpellier, France). None of the participants had participated in a public good game experiment before. There was no significant age difference for men or women (Student's ttest: t p =. 07) although men (on average 23. 1 ± 6. 1 SD) tended to be older than women (on average 21. 8 ± 4. 1 SD). Sociodemographic information was obtained through a questionnaire at the end of each session. We notably collected self-report on sense of smell (normal or impaired during the day of their participation) in order to exclude from analyses the participants who reported to have an impaired sense of smell (N = 6 women, N = 6 men).
AC K NOW L E D GE M E N T S We are very grateful to A. Aublin for his help during experimental sessions and all of the participants in our study. Financial support by PEPS CNRS HomoVox, SINT-ANR-15-CE33-0005-01, ANR Labex IAST and the LABEX Cortex (ANR-11-LABX-0042) within the program Investissements d'Avenir (ANR-11-IDEX-007) is gratefully acknowledged.
Study design Both qualitative and quantitative data for this cross-sectional descriptive mixed-method study were collected using an online survey. It was registered with the data protection officer of the Center National de la Recherche Scientifique (CNRS) and was also approved by the computer science ethics advisory board (Comit e consuLtatif d' Ethique concernant la Recherche en Informatique de Toulouse -CLERIT) under the accreditation number 2018-11-30-10.
Statistical data analysis The data were analyzed using Stata/MP software (version 14, StataCorp, College Station, TX). The Shapiro-Wilk test, recommended for small samples, was first used to test for normality of the data distributions in quantitative variables and led to the choice of nonparametric analyses. Descriptive statistics were performed on all quantitative and qualitative data and served as the basis to describe the clinical practices of French-speaking clinicians, as well as the reported needs and shortcomings. The exact Fisher's test, suitable for small samples and categorical qualitative data, was used to investigate possible differences in self-assessed level of expertise in speech assessment and sufficiency of assessment tools among the clinicians: The relationship between the proportion of SDs in the patient group and the level of expertise in the assessment of SDs.
Results Before addressing the second aim of this studythe description of shortcomings and clinicians' needs in the assessment of SDswe will briefly report on the findings regarding the participants (demographics, initial and continuous training, and characteristics of the SD patient population) and describe the speech disorder assessment practices. Two hundred and seventy-seven replies were submitted. After a database clean-up (removal of duplicates, of entries in the event of dropping out before the first questions regarding the professional activity, and of those with a negative answer to one of the two filter questions), 119 entries were retained. The median time for completion of the questionnaire was 20 min. 47 s. (interquartile range [IQR]: 14 min. 23 s. ).
Thirty-two SLPs (27%) provided answers to the question of the recording/analysis software being used: Praat (n ¼ 17)  [55] , Vocalab (n ¼ 16)  [56] , Audacity (n ¼ 5)  [57] , Diadolab  [58] , and Sound Studio (n ¼ 1)  [59]. The two SLPs using no software are the same SLPs who do not use a microphone. Tasks and measures. The most frequently used tasks are word repetition (95%), spontaneous speech (95%), orofacial motricity and sensitivity tasks (85%), text reading (77%), and sentence repetition (76%). The other less frequently used tasks (less than 69%) are, in decreasing order: semidirected speech, word reading, pseudo-word repetition, sentence reading, pseudo-word reading, phonation tasks (maximum phonation time, voice projection, pneumo-phonic coordination), diadochokinesis, and prosody.
Regarding the lack of validity and reliability, according to the reported desired solutions, clinicians would benefit from new reproducible measures, as well as from clearly defined scoring criteria and reference audio databases to improve subjective ratings, and normative data to benchmark the patients' performances. Still based on the respondents' comments, the practical aspects of using such an assessment tool would be improved by the development of an affordable solution, easy to access as well as to use, with simplified data recording and storing conditions. Portability would also be a great advantage in the clinical contexts. In order to overcome the lack of exhaustivity of currently existing tools, our results show that a specific focus should also be given to certain parameters such as prosody, nasality, and speech rate.
The major use of subjective and qualitative methods highlighted in our data corroborates the results of previous studies, demonstrating a limited use of non-perceptual assessment tools, despite the desire for more reliable means of speech assessment by clinicians given the present subjectivity of both informal and formal assessment  [46, 47, 61]. In order to reduce the subjectivity of the perceptual assessment, several clinicians have expressed the wish for a reference database with pathological speech recordings of varying degrees, in order to better calibrate their judgment of the disorder severity levels. Indeed, numerous studies have shown the improvement of inter-rater reliability through the use of auditory and textual anchors in the assessment of voice and speech samples, in particular through a better perceptual distinction between "healthy" recordings and mild severity levels  [71, 72].
For future assessment tools, it would therefore be interesting to introduce acoustic measures that are more specific to the speech/ phoneme articulation domain, thus allowing the clinician to obtain an analysis directly related to the speech production performance of their patients. It is worth noting that acoustic speech assessment tools are increasingly being developed. The Diadolab software version 3, for example, was released in April 2019, shortly before the end of the online survey completion period. This software includes an assessment and a treatment module, and uses various acoustic speech measures (prosody, phonology, and diadochokinesis). Only one participant reported using this software. The Carcinologic Speech Severity Index (C2SI)  [73]  is an automated index for the assessment of speech disorder severity validated on an oncological population, whose score is computed on three tasks (pseudo-word repetition, text reading, and sustained vowel).
Finally, the computerized MonPaGe protocol, albeit not yet commercialized, seems to be a promising step toward resolving the two main issues discussed in this study. It uses a standardized set of both perceptual and acoustic measures and combines the investigation of analytical modules such as pseudo-words to assess articulatory precision, with more ecological tasks such as communicative interaction situations  [41, 74]. However, acoustic methods are still limited to phonation and vowel measures. Consonants also significantly contribute to speech intelligibility  [75] [76] [77]  and should therefore be further investigated for future assessment tools. Overall, our results show that currently available tools in French-speaking countries do not seem to meet the clinicians' expectations and needs.
Methodological procedure In-depth semi-structured interviews were conducted with male and female French consumers aged 18-70 with various occupations to gauge their spontaneous reactions to one ad. The four authors stopped asking informants to participate when saturation was reached (between 15 and 34 interviews for each ad, see Table  1 ). These sample sizes are in line with requirements for qualitative research and sufficient to obtain data diversity and thematic exhaustiveness  (Guest et al. , 2006). In total, 219 interviews on eight ads were conducted and transcribed (length ranging from 20 to 60 min). For each ad, our sample is aligned with the target of the advertised brand in terms of gender, age, and social milieu. For example, subsamples are balanced between males and females for Leroy Merlin, Faber Castell, Eurostar, and Lego; they are skewed toward males for Jaguar and Land Rover, and toward females for Issey Miyake.
The interview guide focused on the (1) overall perception of the ad, (2) projective associations, (3) understanding of the ad and of the visual metaphor, (4) coherence between an ad's different visual and verbal elements, and (5) attitude toward the ad. The collected material was then analyzed using an iterative process  (Spiggle, 1994)  of searching for recurring patterns and themes. First, the researchers read all transcripts to reveal themes and categories for each type of ad. Then, narratives were clustered and grouped within the different subsets of ads, comparing and contrasting ads with a congruent or a less congruent verbal message, then ads with a hedonic or a non-hedonic food pictorial metaphor. The data was also contrasted for different ads within the same visual structure: for the three ads executing similarity by replacement (Miyake, Lego, Land Rover) and for the two Faber Castell ads.
1 ). Observations were carried out from 9:00 a. m. to 5:00 p. m, when 18 some of the groups observed solely had access to their outside enclosure and the others were 19 free to move between the inside and outside enclosures according to group management and 20 weather conditions. Individuals in the infant age class (0-3 years old) were not included in 21 observation data because they were still dependent on their mothers. 22  We defined "social preference proximity" as a spatial association within one meter of the  Depending on the number of individuals per group, observations were made using two to 3 four 10-minute continuous focal animal samplings per day for each group member. We 4 recorded proximities between individuals but excluded those relating to aggressions, thus 5 retaining proximities that occurred during affiliative or neutral behaviours (see Table  S1   In parallel, instantaneous samplings were collected every 15 minutes and provided 14 information on "positive" proximity patterns (using the same ethogram detailed in 15 Supplementary Material, Table  S1 ).
The weighted networks of familial and bachelor groups are 18 represented as graphs via Gephi software (https://gephi. github. io) (see Supplementary   in familial groups did not reveal any significant differences at social preference (P=0. 373) or 7 social tolerance proximity (P=0. 984). One-way analysis of the three ADO categories of 8 immature males did not reveal any significant differences at social preference proximity, with 9 a value of P=0. 207. However, a trend of significant difference (P=0. 085) was observed at 10 social tolerance proximity. Pairwise permutation tests revealed that the PSI at social tolerance 11 proximity for castrated ADO individuals living in familial groups was significantly higher 12 than that of intact ADO males living in the same context (P adjust =0. 032).
seen within one minute of beginning the scan sampling. Approximately 20 scans were 17 carried out per day to obtain 200 scans per group. 18 Proximity data were discarded from the dataset when aggressive behaviours were 19 recorded, to ensure that we only measured proximities as a proxy of affiliative relationships 20 (stability indicator). We decided to concentrate our analyses solely on a positive pattern of 21 proximities as a "positive index of welfare" because aggressive behaviours are rare in both 22 wild and captive environment (Nakamichi & Kato, 2001; Stokes, 2004). Moreover, additional 23 data collection for aggressive behaviours would have required all occurrences samplings, 24 which was not achievable with a single observer. The data collected from focal animal 25 samplings (in terms of durations), and from the instantaneous samplings (in terms of 1 occurrences) were used to build proximity matrices of the "positive" patterns performed at 2 social preference, social tolerance, then finally general social proximities for each group (network analysis (SNA) required the collection of all dyadic proximity patterns in 6 a studied group.
16 were 5 Social 165 seen within one minute of beginning the scan sampling. Approximately 20 scans were 17 carried out per day to obtain 200 scans per group. 18 Proximity data were discarded from the dataset when aggressive behaviours were 19 recorded, to ensure that we only measured proximities as a proxy of affiliative relationships 20 (stability indicator). We decided to concentrate our analyses solely on a positive pattern of 21 proximities as a "positive index of welfare" because aggressive behaviours are rare in both 22 wild and captive environment (Nakamichi & Kato, 2001; Stokes, 2004). Moreover, additional 23 data collection for aggressive behaviours would have required all occurrences samplings, 24 which was not achievable with a single observer. The data collected from focal animal 25 samplings (in terms of durations), and from the instantaneous samplings (in terms of 1 occurrences) were used to build proximity matrices of the "positive" patterns performed at 2 social preference, social tolerance, then finally general social proximities for each group (network analysis (SNA) required the collection of all dyadic proximity patterns in 6 a studied group.
L. R. -L was funded by a  Marie Skłodowska-Curie Individual Fellowship  from the  EU 's  Horizon 2020 Research and Innovation Programme  (grant no. 794760 ). We also thank the  'Action Transversale' du Muséum National d'Histoire Naturelle (MNHN) , the  UMR 7206 Eco-Anthropologie of the MNHN  and the ' Projet Fédérateur' of the Department 'Homme et Environnement ' for their logistical support. We received no funding for this study.
The main drawback of previous approaches for automatic MiE recognition is the use of the whole face while a MiE occurs only in small parts of the face. These solutions have mainly contributed to the architecture of models for features extraction and classification, which mostly deal with the low intensity and short duration characteristics of MiEs and not the locality character. Nevertheless, recent works  [50, 38, 51]  pay attention to this locality feature. They apply either handcrafted, deep learning or hybrid approaches only on some selected regions, which may result in better performance. Since the MiEs are brief and local, and the avail-able MiEs datasets are very small and unbalanced between classes, the result is still less accurate than the results of MaEs recognition systems. This paper studies a new model to automatically recognize MiE from local facial regions using a composite architecture of CNN and LSTM for spatiotemporal features extraction.
Experiments on 3 spontaneous MiE datasets demonstrate the potential of our approach performance that exceeds the state-of-the-art methods. The remainder of this paper is organized as follows. Section 2 presents the state-of-the-art solutions for MiE recognition. Section 3 investigates the proposed spatiotemporal architecture for MiE recognition. The performance of the proposed solution is assessed and compared to the best-performing solutions in Section 4. Finally, Section 5 concludes this paper.
Deep learning approaches Computer vision tasks like object detection and tracking, video classification, and image segmentation are widely addressed using deep learning techniques. Due to the high performance of deep learning models on these tasks, many researchers have tested and adopted deep learning architectures for MiE analysis. State-of-the-art deep learning solutions are usually based on variants of CNN or a combination of CNN and Recurrent Neural Network (RNN). The first deep learning solution for MiE recognition was proposed by Patel et al  [9]  relying on a CNN. They used the ImageNet-VGG-f CNN  [1]  pre-trained on ImageNet dataset with a CNN trained on facial expressions datasets (CK+  [32]  and SPOS  [35] ) to extract features. Since then, numerous deep learning solutions  [38, 42, 24]  are proposed.
Reddy et al. [38]  proposed a MicroExpSTCNN architecture that is based on 3D-CNN. Lateral Accretive Hybrid Network (LEAR-Net) was proposed by Verma et al. [42]  which is based on a CNN with an accretion layer to refine the salient expression features by accretion of the learning capability of the network. One of the best derivatives of RNN is LSTM. An architecture of CNN combined with LSTM has been proposed by Kim et al. [24]. The CNN enables extracting the spatial features and the LSTM extracts the temporal features from the spatial features. While most computer vision tasks have seen dramatic gains in accuracy, improvements in MiE video analysis have been more moderate. This is mainly caused by the lack of MiE datasets to train deep architectures.
Region definition The region selection is based on two steps. First, we identify the face and then we crop the 6 ROIs identified by Ekman et al. [16]  based on the FACS. Figure  5  shows the locations of the selected regions and the included AUs. To identify the face, the dlib algorithm 2  is used to detect the 68 facial landmarks. Based on these points we crop the face from the entire image. Then, two steps are needed to extract the regions from the face. First, remove all global movements from all frames by referring to the first frame of each sequence: in the used dataset the subjects are static (no global movements), thus it's unnecessary to align the faces. Secondly, we identify 6 blocks corresponding to the selected regions and crop them. Instead of one sequence of MiE we have 6 sequences: one sequence for each region.
And thus, = [ , = 1, … , ], where is the sequence of region and is the number of regions ( is set to 6 in this study). Since the used datasets have different face sizes and knowing that the size of each region is proportional to the size of the face, the size of each region will be different from one dataset to another. To overcome this issue, we resize each region into a fixed size of {(80 × 100), (80 × 120), (60 × 60), (60×160)}, for respectively the left and the right {eye + eyebrow}, the nose, the right and left cheeks, and the mouth.
A drawback of using different labels for each region is the complexity to tune the hyperparameters of the model to fit all the regions. Since the MiE datasets used in our experiments are small, using the proposed labels may help the model to learn faster but also the size of the network is important for this issue. According to  [33, 46] , deep CNN architecture may eliminate the features that are helpful for MiE recognition. Hence, the considered CNN architecture, shown in Figure  8 , is much shallower (only 6 convolutional layers) than state-of-the-art models relying on InceptionNet  [41]  or ResNet  [21]. The proposed CNN model consists of two main parts. The first part includes two sequential blocks, the first block is composed of 4 convolution layers with a filter size of 5 × 5 and the second block consists of 8 convolution layers with a filter size of 3 × 3.
SMIC The Spontaneous Micro-Expression (SMIC) dataset includes spontaneous micro-expressions elicited by emotional movie clips. It contains 164 sequences from 16 different subjects recorded by a high-speed (HS) camera at 100 frames per second (fps). The sequences are recorded also with a normal speed camera at 25 fps of both visual (VIS) and nearinfrared (NIR) light range. So we have three datasets referred as SMIC-HS, SMIC-VIS, and SMIC-NIR for the same MiE recorded under different conditions. SMIC provides sequences with a facial resolution of (190 × 230) and corresponding to only 3 classes: Negative, Positive, and Surprise.
SAMM The Spontaneous Micro-Facial Movement (SAMM) has the largest amount of different ethnicities (13 ethnicities) and age distribution (Mean age = 33. 24). The video sequences are recorded with a high-resolution camera with 200 fps and contain the 7 basic classes of emotion: Disgust, Surprise, Happiness, Fear, Anger, Contempt, and Sadness. The dataset includes 159 sequences from 32 participants. It has the highest spatial resolution (400 × 400 pixels) among these three datasets. Further, this dataset's focus is not on the emotional labels but on the objective AUs so that all the sequences are FACS-coded and include the Onset, Apex, and Offset frames. Table  2  summarizes the three datasets of spontaneous MiEs. These datasets are very interesting since they afford close cases to reality with spontaneous MiEs sequences.
Experimental settings Since the three considered datasets have different conditions (number of classes, frame rate, dimensions), to combine them into a larger dataset, we used two proposed methods: with 3 classes based on the MEGC 2019  [39]  test conditions and with 5 classes based on AUs  [8]. Table  3  summarizes the MEGC conditions, where the number of classes is reduced to 3 (negative, positive, and surprise) to create a new larger dataset, named FULL, that combines SMIC, SAMM and, CASME II. This dataset has 442 sequences with 250 for negative class, 109 for positive class and 83 for surprise. The conditions for the distribution of the 5 classes based on AUs are described in  [8]  and the obtained distribution of classes is summarized in Table  4. The new distribution of classes is used to create a new dataset by fusing only CASMEII and SAMM.
The fusion is done after reassigning the sequences of CASMEII and SAMM datasets to the 7 AU classes presented in Table  4. However, only 5 classes are preserved, the other two classes (VI and VII) are removed because the VI class contains only a very few samples and the "Others" class (VII) denotes movements that are not suited for the other cat- egories and referred to no emotion state. In this evaluation, 4 metrics are used including accuracy, F1-score, UAR, and UF1. The UF1 and the UAR are given by Equations (  8 ) and (  9 ), respectively. 1 = 1 ∑ =1 1 , 1 = 2 2 + + , (8) where , and are respectively true positive, false positive and false negative for class and is the number of classes. = 1 ∑ =1 , = (9) where is the number of samples and is the accuracy rate of class.
Comparison and discussions The proposed model is evaluated with two cross-validation methods: the LOSO-CV method and the Cross dataset (CDB) method. In LOSO-CV, each folder contains more than one sequence of MiE of a person that didn't appear in the other folders. The LOSO-CV ensures subject-independent evaluation. For the cross dataset method, the training set will be from a dataset and the test set from another dataset. For example, the training maybe on CASME II and the test on SAMM. The second method is good to evaluate the model but the provided datasets are too small for the training especially when using a complex architecture of CNN and LSTM which may lead to a bad performance. Tables  10  and 11  show   12  and 13  present the performance with cross dataset method. From Table  10  and Table  11  we can notice that the best performance of our model is reached on the CASME II dataset.
This can be explained by the characteristics of the CASME II dataset with 200 fps (100 fps for SMIC) and RGB color mode (Grayscale for SAMM). The same observation can be made in CDB validation (Table  12  and Table  13 ) where the proposed method outperforms other cases when CASME II is the test set that can support the hypothesis. However, when the CASME II is used as the training set (Table  12 ), we can notice better performance on SAMM, which is a 200 fps and grayscale dataset, than on SMIC, which is 100 fps and RGB dataset. This latter can be an argument to explain how the temporal information is more beneficial for MiE recognition than the color scale of the image which is a part of the spatial information. The proposed solution has been compared with other MiE recognition algorithms in different conditions.