dataesr/scientific-paragraphs-categorization huggingface dataset:
The questions to understand how the system enhances the learning outcome of a learner have been identified as under: -Question 1: How much time is spent on viewing the video, including activation of play and pause buttons on the video player? -Question 2: Whether skimming the video helps in understanding the content? -Question 3: Did the users feel the need to seek to certain parts of the video to find answers to questions which are known beforehand the experiment? -Question 4: Does a digital note assistant help in reviewing and recalling portions of the content in a way that saves time and thus increase the effective learning of the MOOC taker? -Question 5: Did the users who were provided with the MOOCbook module actually refer the downloaded note?.
The data collected through Google analytics is downloadable and hence form our dataset of study. The data downloaded from Google analytics is in the form of csv files which are obtained individually from all the 84 users of the experiment. The effectiveness of the MOOCbook interface was tested using independent-samples t-test. It is aimed to compare means between two unrelated groups on the same continuous variable. In this case, it has been used to understand whether the learning outcome undergraduate engineering student is increased on the application of MOOCbook. Thus the independent variable here is "User of MOOCbook or not "(one of the groups being users who had MOOCbook interface at their disposal and the other being who did not use MOOCbook) and the dependent variable is the "learning outcome ".
Assumptions. As requirement of independent t-test, the 6 point compliance of assumptions as detailed under. 1. The dependent variable, namely learning outcome is measurable in a continuous scale. 2. The independent variable i. e. whether MOOCbook user or not, has two possibilities viz. either the user is given the MOOCbook interface or the user is not. Thus there are two categorical, independent groups. 3. There is independence of observations since there is no relationship between the observations in each group or between the groups themselves. 4. There is no significant outliers, meaning there are no values in the dataset that does not follow the usual pattern. 5. The dependent variable which is learning outcome is approximately normally distributed for each group of the independent variable.
This leads to the conclusion that hypothesis H2 is false and does not hold. Learning Outcome. The Post Test 2 is a questionnaire that aims to find the learning outcome of the participants. The questions contained here are set from the content of the two videos that are hosted in the MOOCbook system. The control group once again is devoid of the functionality of taking notes whereas the treatment group is notes module enabled. The results obtained as shown in Fig. 16  will be directly connected with how much of the lessons depicted within the videos are comprehended by the users. Thus the direct measure of how much a knowledge a learner can retain will be obtained. To analyze the hypothesis H2, a two tailed t-test was done on the sample for 5% level of significance. The mean difference between the treatment and the control group in terms of the Qscores (scores obtained by the participants on the questionnaire) is -2.
Classification We examined several classification methods such as Decision Trees, Multi-Layer Perceptron, Naïve Bayes, and Logistic Regression using Weka toolkit  [6]. 10-fold cross-validation method had been applied to train these classifiers. The results of these classifiers are reported in Table  4  in terms of classification accuracy and root mean square error (RMSE). The classification accuracy refers to the percentage of samples in the test dataset that are correctly classified (true positives plus true negatives). Root-mean-square error (RMSE) is frequently used as measure of the differences between values predicted by a classifier and the values expected. In this experiment, it provided the mean difference between the predicted students' comprehension level and the expected comprehension level.
g. , fascinated with wild life) and the learning styles (e. g. , attempts the easier assignments first) of each student. The same information can be also provided by custom educational software (i. e. , CognitOS). • Sophisticated cameras (e. g. , RGB-D camera such as Microsoft Kinect) that track the head pose of the learner and are used as a surrogate for gaze. The combination of eye-tracking and head pose tracking algorithms offers an accurate overview of what the students are looking at on the computer screen and on whom or what they are focused on (e. g. , teacher, class board, etc. ). Moreover, the use of cameras is ideal for tracking the body posture and the direction of an individual student, especially when taking into consideration that they constantly move even while seated. Besides learners' orientation, camera input also enables the identification of specific gestures that indicate whether a student is paying attention to the lecture or not (e.
Apparatus A driving simulator (Keteng steering wheel and City car driving software version 1. 4. 1) was used in this study. The city car driving is a car simulator, designed to help users feel the car driving in a city or a country in different conditions. Special stress in the City car driving simulator has been laid on the variety of road situations and realistic car driving. IView X head mounted eye-tracking device (SensoMotoric Instruments) was used to record participants' dominant eye movements. Software configuration has the video recording and the BeGaze version 3. 0 eye movement data analysis, sampling rate 50/60 Hz (optional 200 Hz), tracking resolution, pupil/Corneal reflection <0. 1° (typical) and gaze position accuracy <0. 5°-1. 0° (typical). ANSWatch TS0411 was used to measure the heart rate (HR) and HRV (heart rate variability) data.
Predicting the Number of Errors by Integrating Physiological Measures Six physiological indices, including pupil dilation (X 1 ), blink rate (X 2 ), blink duration (X 3 ), fixation duration (X 4 ), HR (X 5 ) and SDNN (X 6 ) into a synthesized index and to establish a model of work performance, this study used the GMDH method and the predictive modeling software DTREG version 10. 6. The ratio of training and testing in this study was selected as 80%:20% to fit in with the available experimental sample size of 26. Each input variable (X i ) was normalized to a range of 0 and 1 before the training and testing process begins. The network was trained by using a random training data set, and the training data was also never used in the test data. The results indicated that physiological indices of X 1 , X 2 , and X 5 were the best significant predictor factors in the performance by the subject.
Method Let the conditional probability of experiencing an emotion emo n given that an emotion emo m is expressed be denoted by P t (emo n |emo m ). We evaluate P t (emo n |emo m ) using a Bayesian framework. These are then normalized over the space of all expressed emotions emo m to obtain the probabilities of various experienced emotions emo n. First, an emotion recognition algorithm is run on the end-user's texts to determine the probabilities of various expressed emotions. These probabilities serve as priors in the Bayesian framework. Next, we leverage large datasets containing emotional content across many people (such as blogs, etc. ) to measure the similarities between words corresponding to a pair of emotions. This information is computed across several people and is reflective of the general relatedness between two emotion-indicating words (for example, between the words "sad" and "angry").
Estimation of Likelihoods We estimate similarities between words corresponding to a pair of emotions by training neural word embeddings on large datasets  [9]. This similarity gives a measure of relatedness between two emotion-indicating words in a general sense. For example, if the word "sad" has higher similarity with word "anger" than with the word "worry", then we assume that the relatedness between emotions "sad" and "anger" is higher than the relatedness between "sad" and "worry". This may not necessarily be true with respect to every user, but is true in an average sense since the calculation is based on very large datasets of emotional content across several people. Since this measure is data-dependent, we have to choose appropriate datasets containing significant emotional content to get reliable estimates.
Estimating Conditional Probabilities We employ a Bayesian framework to integrate emotion priors with the likelihood probabilities. Let P p (emo n ) be the prior probability of an emotion emo n as obtained from an emotion analysis algorithm, and P l (emo m |emo n ), be the likelihood probability of emo m given emo n , obtained by using appropriate training dataset. Then, the posterior probability of experiencing an emo n given an expressed emotion emo m is given by P t (emo n |emo m ) = P l (emo m |emo n )P p (emo n ) all-emo P l (emo m |emo n )P p (emo n ) (2) The above quantity is specific to the user under consideration.
Dataset We studied the performance of the algorithm on a dataset consisting of 16 anonymous user conversations with a human listener spanning a total of more than 20 h. Conversations between users and human listener dealt with a variety of topics such as relationship issues, emotional wellbeing, friendship problems, etc. On average, the conversation between a user and the human listener lasted approximately 30 min. Some of these conversations lasted more than an hour (the longest was 70 min) while some lasted only 10 min. We divided the conversations into segments corresponding to the time a user spoke uninterrupted by a human listener. For convenience we refer to each segment as a "transcript". Transcripts numbered A. x are all contiguous parts of the same conversation A. There were over fifty transcripts in the dataset.
Eight speakers -balanced in sex -aged from 25-48 years (mean 32 years) took part in the recording. The 431 clips have an average duration of 8. 4 s presenting six emotions. The Audiovisual Interest Corpus (AVIC) (cf. [20] ) contains samples of interest. The scenario setup is as follows: A product presenter leads each of the 21 subjects (ten female) through an English commercial presentation. The level of interest is annotated for every sub-speaker turn. The Danish Emotional Speech (DES) (cf. [8] ) data set contains samples of five acted emotions. The data used in the experiments are Danish sentences, words, and chunks expressed by four professional actors (two females) which were judged according to emotion categories afterwards. The Berlin Emotional Speech Database (emoDB) (cf. [2] ) is a studio recorded corpus.
In our experiments we used a clustering provided by  [21]  mapping the original arousal-valence space into 4 quadrants. The SmartKom (cf. [28] ) multi-modal corpus provides spontaneous speech including seven natural emotions in German and English given a Wizard-of-Oz setting. For our experiments, we used only the German part. The Speech Under Simulated and Actual Stress (SUSAS) (cf. [14] ) dataset contains spontaneous and acted emotional samples, partly masked by field noise. We chose a corpus' subset providing 3593 actual stress speech segments recorded in speaker motion fear and stress tasks. Seven subjects (three female) in roller coaster and free fall stress situations utter emotionally coloured speech in four categories. The Vera-Am-Mittag (VAM) corpus consists of audio-visual recordings taken from a unscripted German TV talk show (cf.
Acknowledgements. The work of AO has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement No. 637991 ). Acknowledgements. The authors would like to thank  Fraunhofer First, Intelligent Data Analysis Group , and  Campus Benjamin Franklin of the Charite' -University Medicine Berlin  (http:// www. bbci. de/competition/iii), and the  Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), Graz University of Technology  (http://www. bbci. de/competition/iv), for providing the dataset online. Acknowledgements. The authors would like to thank the reviewers for their valuable remarks and comments. Also, the authors thank the participants who helped conduct this research. Acknowledgement.
This work is carried under the grant of  TÜBİTAK -The Scientific and Technological Research Council of Turkey  to Project No:  115E469 , Identification of Multi-word  Expressions in Turkish Texts. We thank to  Mehmet Taze ,  Hande Aka Uymaz ,  Erdem Okur  and  Levent Tolga Eren  for their efforts in labeling MWE data set. Acknowledgments. We acknowledge continued support by the  Transregional Collaborative Research Centre   SFB/TRR 62  " Companion-Technology for Cognitive Technical Systems " (www. sfb-trr-62. de) funded by the  German Research Foundation (DFG). Further, we thank the project " Mod3D " (grant number:  03ZZ0414 ) funded by  3Dsensation  (www. 3d-sensation. de) within the  Zwanzig20 funding program  by the  German Federal Ministry of Education and Research (BMBF).
Light transmission aggregometry (LTA) Platelet-rich plasma (PRP) and autologous platelet-poor plasma (PPP) were obtained by centrifugations of citrated blood as recommended. 23 photometric method on an 8-channel aggregometer (PAP-8E, Bio/ Data Corporation, Horsham, PA, USA) was used. Platelet-rich plasma was incubated 2 minutes at 37°C and was then stirred at 122 g for 2 minutes before adding saline, ADP or AA (5 μmol L -1 and 1 mmol L -1 , respectively). The platelet response was recorded during 5 minutes. Platelet aggregation was quantified as the percentage of maximal optical change. In the last version of the BH's guidelines, when ADP-induced platelet aggregation is <50%, no dose change of dipyridamole is required; when AA-induced platelet aggregation is <30%, no dose change of aspirin is required.
From the beginning of TEG-PM monitoring, our ability to make therapeutic decisions was compromised by the unusual tracings obtained with activator F. We wondered how MA fibrin was determined, mostly because MA fibrin never stabilized with questionable numerical information provided by the TEG software (MA fibrin and calculated percentage of platelet inhibition). Nelles and Chandler described this pattern demonstrating that platelet activation occurred in 28% of heparinized blood samples, generating a rising unstable MA fibrin curve and, consequently, an overestimation of the percentages of ADP and AA inhibition. 16 In that study, patients were critically ill (79% had left VAD). Eptifibatide use corrected the tracing and decreased the percentage of platelet inhibition. Our study shows that atypical pattern also occurred in critically ill children.
Root system analysis is a complex task, often performed with fully automated image analysis pipelines. However, the outcome is rarely verified by ground-truth data, which might lead to underestimated biases. We have used a root model, ArchiSimple, to create a large and diverse library of ground-truth root system images (10,000). For each image, three levels of noise were created. This library was used to evaluate the accuracy and usefulness of several image descriptors classically used in root image analysis softwares. Our analysis highlighted that the accuracy of the different traits is strongly dependent on the quality of the images and the type, size, and complexity of the root systems analyzed. Our study also demonstrated that machine learning algorithms can be trained on a synthetic library to improve the estimation of several root system traits.
Creation of a Root System Library We used the model ArchiSimple, which was shown to allow the generation of a large diversity of root systems with a minimal amount of parameters  (Pagès et al. , 2013). To produce a large library of root systems, we ran the model 10,000 times, each time with a random set of parameters (Figure  2A ). For each simulation, the growth and development of the root system were constrained in two dimensions. The simulations were divided into two main groups: fibrous and tap-rooted. For the fibrous simulations, the model generated a random number of root axes and secondary (radial) growth was disabled. For tap-root simulations, only one root axis was produced and secondary growth was enabled (the extent of which was determined by a random parameter). The root system created in each simulation was stored in a Root System Markup Language (RSML) file.
Each RSML file was then read by the RSML Reader plugin from ImageJ to extract ground-truth data for the library  (Lobet et al. , 2015). These ground-truth data included geometrical and morphological parameters (Table  1 ). For each RSML data file, the RSML Reader plugin also created three JPEG images (at a resolution of 300 DPI) for each root system. To simulate one type of image degradation, we added different levels of noise to the images (using the Salt and Pepper Filter in ImageJ) (Figure  2D ). For each root system, we computed overlapping index as the number of root segments having an overlap with other root segments over the total number of root segments.
Data Analysis Data analysis was performed in R (R Core Team) 1. Plots were created using ggplot2  (Wickham, 2009)  and lattice  (Sarkar, 2008 ). 1 R Core Team R: A Language and Environment for Statistical Computing. The Mean Relative Errors (MRE) were estimated using the equation: MRE = n 1 |y i -y i| y i n where n is the number of observations, y i is the ground-truth and y i is the estimated ground-truth.
In a machine learning setting one is given a set D = {(x 1 , y 1 ), (x 2 , y 2 ),. , (x n , y n )}, where x i = (x 1 i , x 2 i. , x s i ) is an element of a s-dimensional feature space X, and y i = (y 1 i , y 2 i ,. , y t i ) an element of a t-dimensional response space Y. The learning task is to find a model M : X → Y that predicts the data in a good way, where goodness is measured with regard to an error function L. A decision tree T D is a machine learning method that, for a dataset D, constructs a binary tree with each node representing a binary question and each leaf a value of the response space. In other words, a prediction can be made from an input value by looking at the set of binary questions that leads to a leaf (e. g. , is the first-order root bigger than q1 and if yes is the number of second-order roots smaller than q2 and if no,.
). Each decision is based upon exactly one feature and is used for deciding which branch of the tree a given input value must take. Hence a decision tree splits successively the set D into smaller subsets and assigns them a value y i = T D (x i ) of the response space. The choice of the feature used for splitting depends on a relevance criterion. In our setting, the default relevance criterion from the randomForest R package  (CRAN randomForest, 2015) , namely the Gini index, has been used. A Random Forest  consists of l decision trees T D,k , where several key parameters such as the feature space, are chosen randomly (hence the word Random in the algorithm name). While using a random subspace strongly accelerates the growth of a single tree, it can also decrease its accuracy. However, the use of large number of trees counterbalance advantageously those two effects.
Preprocessing Missing values in our dataset might arise due to highly noisy images, where the measurement of certain descriptors has been infeasible. To deal with this issue, we first replaced missing values. This is done using the imputation function of the randomForest R package. It replaces all missing values of a response variable by the median and then a Random Forest is applied on the completed data to predict a more accurate value. We favored 10 trees for computing the new value over the default value of 300 as we found that it offered sufficiently accurate results for our application while being much faster.
RESULTS AND DISCUSSIONS Production of a Large Library of Ground-Truth Root System Images We combined existing tools into a single pipeline to produce a large library of ground-truth root system images. The pipeline combines a root model (ArchiSimple,  Pagès et al. , 2013) , the Root System Markup Language (RSML) and the RSML Reader plugin from ImageJ  (Lobet et al. , 2015). In short, ArchiSimple was used to create a large number of root systems, based on random input parameter sets. Each output was stored as an RSML file (Figure  2A ), which was then used by the RSML Reader plugin to create a graphical representation of the root system (as a. jpeg file) and a ground-truth dataset (Figure  2B ). Details about the different steps are presented in the Materials and Methods section. We used the pipeline to create a library of 10,000 root system images, separated into fibrous (multiple first order roots and no secondary growth) and tap-root systems (one first order root and secondary growth).
The ranges of the different ground-truth data are shown in Table  3  and their distribution is shown in the Supplemental Figure  1. We started by evaluating whether fibrous and tap-root systems should be separated during the analysis. We performed a Principal Component Analysis on the ground-truth dataset to reduce its dimensionality and assess if the type grouping influenced the overall dataset structure (Figure  3A ). Fibrous and tap-root systems formed distinct groups (MANOVA p < 0. 001), with limited overlap. The first principal component, which represented 30. 9% of the variation within the dataset, was mostly influenced by the number of first-order axes. The second principal component (19. 1% of the variation) was influenced, in part, by the root diameters. These two effects were consistent with the clear root system type grouping, since they expressed the main difference between the two groups of root-system types.
Different types of information are highlighted in Figure  4. First, using a ground-truth image library allows for a quick and systematic analysis of all the descriptors extracted by the image analysis pipeline. Second, it allows researchers to identify which traits can be accurately evaluated (or not) and by which descriptors. Third, for some groundtruth data, such as the mean length of second order roots or the number of first order roots, it shows that none of the classical descriptors gave a good estimation (Figure  4 , highlighted with arrows). Finally, the figure highlights that some correlations were different for fibrous-and tap-root systems. As an example, the correlation found between the mean_2+_order_diameter and diam_mean estimators was better for fibrous roots than within the tap-root dataset.
Using the Synthetic Library to Train Machine Learning Algorithms The main advantage of creating a synthetic library is to generate paired datasets of image descriptors and their corresponding ground-truth values. Having information on both can, in theory, be used to either calibrate the image analysis pipeline or to identify the best descriptors for the ground-truth traits of interest. Here, we explored the second approach and used a Random Forest algorithm to find which combination of descriptors would best describe each ground-truth data (see Material and Methods for details). In short, we randomly divided the whole dataset into training (3/4) and testing subsets (1/4). The training set was used to create a Random Forest model for each ground-truth data, which was then we applied to the test set.