dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset and Task The Sussex-Huawei Locomotion-Transportation (SHL) dataset is main source of the challenge 2. The dataset was recorded by three participants engaging in 8 transportation and locomotion activities in real-life settings: Still, Walk, Run, Bike, Car, Bus, Train, and Subway. Each participant carried four smartphones at four body positions simultaneously: in the hand, at the torso, in the hip pocket, in a backpack or handbag. The complete dataset contains 2812 hours of labeled data and 16 sensor modalities  [2, 5]. It is considered as one of the biggest dataset in the research community. The SHL recognition challenge used the data recorded by the first participant with the phone at the pocket position in 82 days (5-8 hours per day). The challenge uses 62 days as the training dataset and 20 days for the testing dataset.
The challenge dataset provides the raw data from accelerometer, gyroscope, magnetometer, linear accelerometer, gravity, orientation, and pressure. The sampling rate of all the sensors is f s = 100 Hz. Fig. 1  depicts the duration of each class activity in the training and the testing dataset  [10]. In this challenge, both the training and the testing data are given in the form of 1minute segments, with their temporal order randomized. The objective of the challenge is to train a classifier using the training dataset and then apply this classifier to recognize the transportation mode of sequences in the testing dataset. The recognition performance is evaluated with F1 2 http://www. shl-dataset. org score averaged over all of the activities. We additionally measure the global recognition accuracy over all the data samples.
Method (a) Processing Pipeline Fig. 2  depicts the processing pipeline for predicting the transportation mode from the multimodal sensor data. For the training dataset, the sensor data from M modalities are segmented into L t short frames: {s t (1), • • • , s t (l), • • • , s t (L t )} , where s t (l) represents the sensor data in the l-th frame. Given the labels in these frames, {c t (1), • • • , c t (l), • • • , c t (L t )},.
(b) Sensor Input We consider three inertial sensors for this recognition task, i. e. accelerometer, gyroscope, magnetometer. Each sensor contains three channels of measurement along the x-, y-, and z-axis of device. Since the pose and orientation of the smartphone is unknown, we combine the three channels by computing the magnitude, i. e. Acc = Acc 2 x + Acc 2 y + Acc 2 z ( 1 ) Gyr = Gyr 2 x + Gyr 2 y + Gyr 2 z (2) M ag = M ag 2 x + M ag 2 y + M ag 2 z (3) For the training dataset, we slide through the magnitude sensor data in each 1-minute segment with a window of length 5 seconds and skip size of 2. 5 seconds, generating framed data each containing 500 samples. For the testing dataset, we similarly slide through each 1-minute segment with a window of length 5 seconds and skip size of 5 seconds.
In the following, we discuss the details of the three types of classifiers. (c) Classical pipeline Fig. 4  illustrates the pipeline for predicting the transportation mode with a classical classifier, which uses as input the hand-crafted feature vector s H. A normalization procedure, which maps each feature into the range [0, 1], is applied before feeding the features to the classifier. This step aims to increase the robustness of the classifier. Let's use the i-th feature f i as an example. Suppose Q 95 i and Q 5 i are the quantile 95 and quantile 5 of f i across all the frames in the training dataset. The normalization of each frame is performed as The features in the testing dataset are normalized in the same way using Q 95 i and Q 5 i , which are computed in advance from the training dataset.
After normalization, the new feature vector in the l-th frame is denoted as sH (l). fi (l) ← min max f i (l) -Q 5 i Q 95 i -Q 5 i , 0 , 1. (4) Table  1  lists the five classical classifiers that are considered for the recognition task: NB, DT, RF, KNN and SVM. The first four classifiers are implemented with Matlab Machine Learning Toolbox, while SVM is implemented with LIB-SVM  [1]. All the classifiers use default parameters set in the library functions unless explicitly mentioned. For instance, the KNN Matlab function uses a default value of K = 1. We can also set the parameters manually. For instance, in DT we set the parameter 'minleafsize = 1000'; in RF we set the number of trees as 20 and in each tree the parameter 'minleafsize = 1000'. We use the Matlab Deep Learning Toolbox to implement the FC classifier.
Results For data processing we use a computer equipped with an Intel i7-4770 4-core CPU @ 3. 40 GHz with 32 GB memory, and a GeForce GTX 1080 Ti GPU with 3584 CUDA cores @ 1. 58 GHz and 11 GB memory. The code is written with Matlab 2018a, calling functions from the Machine Learning Toolbox and the Deep Learning Toolbox. As indicated in Fig. 2 , we use the training dataset (375,130 frames) to train the classifier, and then use the testing dataset (68,376 frames) to evaluate the performance. Table  4  summarizes the global accuracy and the F1 score before and after post-processing for each classifier, and the processing time for training and testing. The two measures, global accuracy and F1 score, do not show big difference for most classifiers. For each classifier, post-processing can improve the recognition performance effectively (F1 score by above 10%).
Conclusion We compared the transportation mode recognition performance obtained by classical and deep-learning pipelines, with input from hand-crafted features, time-domain raw data and frequency-domain raw data of the SHL challenge dataset. Unsurprisingly, random forest is highly recommend among the five classical classifiers, trading off the classification performance and the computational time. Deep-learning classifiers achieve better performance than classical ones at the cost of longer training time. CNNbased classifier operating on frequency-domain raw data achieves the best performance among all the classifiers. The post-processing scheme can improve the recognition performance remarkably. The paper mainly aims to provide a reference performance for comparison with the results from challenge participants (which will be presented in  [10] ).
Funding sources This work was supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the programs  LICORNES  ( ANR-12-CULT-0002 ) and  ASCE  ( ANR-13-PDOC-0004 ) and by the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement n°[609819], SOMICS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Five weeks before polling day, Ruth Davidson was therefore summoned to London by May's Scottishborn campaign manager, Fiona Hill, where she argued her case and pleaded for Populus' involvement. Returning to Scotland with the funds she needed, the Scottish Conservative leader immediately launched a targeted social media campaign tailored to differentiate the different target voters: SNP Leavers, Lib-Dem 'cultural Tories' and Labour Unionists. 26 anvassing data was constantly fed into the Populus model during the campaign while the Conservative campaign poured its funds and efforts into winning the constituencies which appeared on Kanagasooriam's "latent line". The Scottish Conservatives targeted little more than thirteen key seats, many of which were in north-east of Scotland, such as Aberdeen South, West Aberdeenshire and Kincardine, and Banff and Buchan, where the Tories had performed well in 2016 and which were also strong Leave areas in the EU referendum.
Material produced in numerous social media posts that were made during the movement by young participants is also used. Many of the texts (manifestoes, testimonies and articles, etc. ) produced during Nuit Debout are published in Nuit Debout: Les Textes  [4]. Other references are made to a special issue on the Nuit Debout in the French academic journal Les Temps Modernes  [5]  (for further information see footnotes and reference list). Finally, one of the authors of this article was an independent observer of the Nuit Debout in Paris. For the economic data we mostly use official French statistics from the Institut national de la statistique et des études économiques  (Insee)  and the Institut national de la jeunesse et de l'éducation populaire (Injep).
Materials and Methods With the aim to explore the attitudes, knowledge and expectations of perinatal health professionals, we conducted a prospective observational study using a self-administered questionnaire. The method involved a "triangulation" strategy in which both qualitative and quantifiable data were collected and analysed to address both subjective and objective aspects of the problem of environmental health. The secondary objective of the study was to be able to propose training schemes adapted to professional needs and wishes. No nominative data were entered in the data base; our study was, therefore, not subjected to institutional approval in accordance with French human research law.
Study Design Two identical versions of the questionnaire were disseminated. One online version was sent to HPs, using the directory of the perinatal health professional network "Réseau Mediterranée" PACA-CORSE-MONACO, which includes up to 5000 professionals' e-mail addresses. A printed version was distributed during scientific meetings or provided to health establishments upon request. Data were collected between January and April 2017 by a standardized anonymous self-administered questionnaire. The questionnaire was structured with five parts. To first explore perinatal HPs' representations, we chose the free association method using "environmental health" as an inductive expression. HPs were asked to give three words or expressions. Note that they were informed of the WHO EH definition to avoid any confusion with the semantic of ecology.
Thematic Content Analysis To analyse the content of the free words or expression associated to environmental health, we used the content analysis approach as originally described by Bardin  [20]. Theme and sub-categories were determined after familiarisation with the data. Qualitative data from the questionnaires were analysed, using a thematic analysis chart that was designed to identify the themes cited and to make comparisons to highlight recurrent issues and differences. Frequency of words and expressions occurrences were determined using Excel.
Statistical Analysis Quantitative data were extracted from the questionnaires and analysed using Excel (Microsoft, Redmond, WA, USA) and SPSS software (IBM Analytics, Chicago, IL, USA). Descriptive and variance analysis were performed to compare the responses of each group. The internal consistency of the data was tested using Cronbach's alpha coefficient, which was considered satisfactory from 0. 7  [21]. To allow variance and Chi-square test analysis, we formed three groups of occupation (population size > 30): midwives (G1), physicians (G2) and non-medical professions (G3). Regarding the area of practice, two Alpine areas (Alpes de Haute-Provence and Hautes-Alpes) were combined. Corsica and Monaco were not considered, as the figures were too small. Finally, establishments of health, licensed professionals, Mother and Infant Protection Centres and nurseries were the modes of professional practice considered.
This research was co funded by ARS PACA (Regional Agency for Health Provence-Alpes-Cote d'Azur, C 2016-000486) and Région PACA, C2016-11478. This work has been carried out thanks to the support of the A*MIDEX project «CREER» (n • ANR-11-IDEX-0001-02) funded by the «Investissements d'Avenir» French Government program, managed by the French National Research Agency (ANR).
Acknowledgments: Authors thank all the perinatal health professionals who responded to the questionnaire. Authors tanks the institutions that sent out e. mails to take part in the survey. Authors wish to specially thank  M. Andrieu Semmel  and  S. Urban-Boudjellab  from department " Santé Environnement" and H. Meur from department of "prevention, promotion de la santé " of the  "Agence Régionale de Santé" PACA  who both supported the study and participated to the study steering committee. We also thank  A Claquin  from  Réseau Méditerranée  for his administrative support and participation to the study steering committee.
Funding: This research was co funded by ARS PACA (Regional Agency for Health Provence-Alpes-Cote d'Azur, C 2016-000486) and Région PACA, C2016-11478. This work has been carried out thanks to the support of the A*MIDEX project «CREER» (n • ANR-11-IDEX-0001-02) funded by the «Investissements d'Avenir» French Government program, managed by the French National Research Agency (ANR).
Statistical analysis We addressed the relations between the multidimensional and longitudinal child's behavior data within the structural equation modeling (SEM) framework. To deal with the multidimensional data, we defined latent variables representing internalizing, and externalizing behaviors at age 6, as suggested by Goodman et al. [20]  for the SDQ, and we applied the same rationale at age 2 (Fig. 1 ). The internalizing behavior trait was thus defined at age 2 by the emotional symptoms subscale, at age 6 by the emotional symptoms and peer relationship problems subscales. The externalizing behavior trait was defined at age 2 by the attention deficit/hyperactivity, aggression and opposition subscales, at age 6 by the hyperactivity/inattention and conduct problems subscales. Covariance between the two latent traits at each age was estimated.
Aim of This Study To help design and implement future digital health strategies and interventions in university campuses, this exploratory study aimed to provide a general overview on patterns of digital health use among university students in France, extending existing research with updated data on Web-based health-related information seeking and related trustworthiness, and on the use of mobile phone apps and wearable devices for health and well-being. The correlation of digital health use with sociodemographic characteristics and self-rated health was also examined.
The quota sampling method was used to recruit students according to their gender and field of study : the interviewers had to approach a predefined number of female and male students in each campus to obtain a representative sample of students according to the student registration database of the University of Bordeaux 2016/2017 (see Multimedia Appendix 1). If students consented to participate in the study, the questioning proceeded after the signature of a written informed consent form. If eligible students declined to participate, interviewers asked them why and documented the reasons for refusal. The inclusion criteria were currently studying in 1 of the 4 university campuses in Bordeaux, France; being French-speaking; and being aged 18-24 years. We excluded those aged 25 years and older because, according to the Bologna process ensuring comparability in the standards and quality of higher education qualifications in Europe, the average age of entrants to the university is 18.
Statistical Analysis Statistical analysis was performed using SAS (V. 9. 4; SAS Institute Inc, Cary, NC, USA). Descriptive statistics (eg, means and SDs) were used in the initial data analysis. Chi-square and Fisher exact tests were used to identify associations between sociodemographic characteristics, self-rated general and mental health, and digital health use of the study participants. For the tests of independence, digital health use was summarized in the following 5 components: (1) possessing a mobile health app, (2) possessing a health-related wearable device, (3) searched Web-based health-related information and support topics (for all reasons), (4) consulted Web-based sources for health-related information and support (for all degrees of credibility), and (  5 ) searching online for a health professional or service.
Acknowledgments The authors are indebted to the participants of the i- Share  project for their commitment and co-operation and to the entire i-Share team for their expert contribution and assistance. The authors would like to especially acknowledge  Amélie Capelle  and all students administering the questionnaire. This research was financed by  IReSP  ( Institut de Recherche en Santé Publique-Research Institute in Public Health ), call " Services de Santé-Health Services ," 2016.
In this article, we present a large 3D motion capture dataset of Taijiquan martial art gestures (n ¼ 2200 samples) that includes 13 classes (relative to Taijiquan techniques) executed by 12 participants of various skill levels. Participants levels were ranked by three experts on a scale of [0-10]. The dataset was captured using two motion capture systems simultaneously: 1) Qualisys, a sophisticated optical motion capture system of 11 cameras that tracks 68 retroreflective markers at 179 Hz, and 2) Microsoft Kinect V2, a low-cost markerless time-of-flight depth sensor that tracks 25 locations of a person's skeleton at 30 Hz. Data from both systems were synchronized manually. Qualisys data were manually corrected, and then processed to complete any missing data. Data were also manually annotated for segmentation.
Both segmented and unsegmented data are provided in this dataset. This article details the recording protocol as well as the processing and annotation procedures. The data were initially recorded for gesture recognition and skill evaluation, but they are also suited for research on synthesis, segmentation, multi-sensor data comparison and fusion, sports science or more general research on human science or motion capture. A preliminary analysis has been conducted by  Tits et al. (2017)  [1] on a part of the dataset to extract morphology-independent motion features for skill evaluation. Results of this analysis are presented in their communication: ".
Data This brief article presents a multimodal motion capture (MoCap) dataset of Taijiquan martial art gestures. The data were initially recorded for gesture recognition and skill evaluation. The dataset includes 2200 sequences of 13 classes (relative to different Taijiquan techniques) performed by 12 participants of different levels of expertise. Participant levels have been ranked by three Taijiquan teachers (on a scale of 0-10). The dataset contains both unsegmented and manually segmented sequences. The data were captured using the Qualisys optical motion capture system and the second version of the Microsoft Kinect simultaneously. The Qualisys motion capture system used consists of 11 high-speed infrared cameras that track 68 retroreflective markers placed over the performer's body, at a frame rate of 179 Hz.
The Kinect sensor, on the other hand, is a low-cost time-of-flight depth sensor that estimates 25 3D joints locations at a frame rate of approximately 30 Hz. A subset of this dataset has already been used in a previous research  [1]  to validate a method of morphology independent feature extraction in MoCap data for skill evaluation. To the authors' knowledge, it is the first dataset of sports gestures comprising simultaneously a large number of participants (12), a large number of different classes (13), and a variety of skill levels, and captured with two different motion capture systems.
Participants Twelve participants volunteered to participate in the dataset recordings. All of them attended courses in the Taijiquan school Eric Caulier, 1  and were assigned a category according to their level: Novice, Intermediate, Advanced or Expert (three teachers of the school). Each Taijiquan teacher also provided individual rankings for each participant, on a scale of 0-10. These rankings were provided independently by each teacher, from their personal knowledge of all the participants during courses. Relevant personal details for each participant, including age, height, weight, gender, practice experience and skill level can be found in Table  1.
Data processing Qualisys MoCap data were manually corrected using the Qualisys Track Manager (QTM) software. 2 he corrected data were then extracted in standard 3D motion data formats (C3D and TSV). All missing data (generally due to marker occlusions) were estimated with an automatic MoCap data recovery method. 3 he Kinect data were saved into ". txt" files which contain several lines corresponding to each captured frame. Each line contains one integer number (ms), relative to the moment when the frame was captured, followed by 3 Â 25 float numbers corresponding to the 3-dimensional locations of the 25 body joints.
Manual annotation (segmentation) All renditions were manually labeled from Qualisys data to identify beginning and ending of each instance of a gesture. To that end, the MotionMachine framework  [3]  was used. The annotation software created from this framework 4  allows mouse-controlled simultaneous visualization of 3D movements (Qualisys data), and 2D curves displaying temporal evolution of each  coordinate of their Center Of Mass (COM), estimated from the mean position of the 68 markers. COM coordinates can be used as a global visual indication for systematic segmentation, as described in Table  5. In the software, the time of the MoCap sequence is controlled by the horizontal position of the mouse, and any mouse click creates a label at its current position. The GUI then allows the edition of the label list.
Fig. 2  shows an example of the annotation procedure. In this example, gestures G06 and G07 are being annotated. From annotations, Qualisys data were automatically segmented using the MoCap Toolbox for Matlab  [4]  and MoCap Toolbox extension. 5 All unsegmented files were named using the convention 'PppTttCcc' (e. g. P01T01C01) for which 'pp' is the performer ID (see Table  1 ), 'tt' is the type of the sequence (see Table  4 ) and 'cc' is the number of the clip (repetition of the same sequence). All segmented files were named using the convention 'PppTttCccGggDddSss' (e. g. P01T01C01G01D01S01). 'gg' indicates the gesture (see Table  3 ), 'dd' indicates the direction (01 for left and 02 for right -Table  4  Types of renditions performed by the participants.
Data synchronization The data from both Qualisys and the Kinect were synchronized with the use of the MotionMachine framework. One important feature of this framework is the management of timed sequences. This allows the synchronization of the data by means of time and not by frame indexes. For each unsegmented sequence, the delay between files was estimated using the MotionMachine framework (see Fig. 3 ), and the data were manually synchronized by removing the first extra frames from the longest sequence.
Acknowledgements Mickaël Tits is funded through a Ph. D. grant from the  Fonds pour la Formation à la Recherche dans l'Industrie et l'Agriculture (FRIA) ,  Belgium , under the grant number  FC 005499. This research is partially funded by the  European Regional Development Fund (ERDF)  under the grant number  ETR 1212 0000 3303. The Qualisys motion capture system used in this research was funded by the  Fonds de la Recherche Scientifique (FRS-FNRS) ,  Belgium , under the equipment number  EQP U. N041. 13. The authors thank all the participants who volunteered for the recordings, and especially the Taijiquan teachers  Georgette Methens-Renard  and  Michele Orszagh  for their help in guiding the recording sessions, and ranking all participants.