dataesr/scientific-paragraphs-categorization huggingface dataset:
These problems can be overcome by combining classical bioassay-guided fractionation with machine learning approaches to reveal the identity of bioactive natural products in extracts without the need for purification to homogeneity. In our opinion, the latter combination approach holds promise to accelerate discovery because many antiviral in vitro assays can be performed without handling live viruses and because identification of polyphenols (and other natural products) has become easieroverthe past severalyears thanksto advances in plant metabolomics and the ever growing natural product databases such as Phenol-Explorer  [67] ,KnapSack  [68] , and the Global Natural Product Social Networking (GNPS) database  [69].
Acknowledgements B. B. acknowledges the financial support from the  Slovenian Research Agency  (project grant No. J1-9109  and research core funding No. P1-0297 ). M. V-P. acknowledges the financial support from the  Paris-Nord University , the  Franco-Belgian  " Tournesol "  PHC  project grant No. 42703TD  and the  LIA INFINIS/SINFIN (CNRS France -CONICET Argentina ).
In the context of graph layout, many algorithms have been designed to remove node overlapping, and many quality criteria and associated metrics have been proposed to evaluate those algorithms. Unfortunately, a complete comparison of the algorithms based on some metrics that evaluate their quality has never been provided and it is thus difficult for a visualisation designer to select the algorithm that best suits their needs. In this paper, we review 22 metrics available in the literature, classify them according to the quality criteria they try to capture, and select a representative one for each class. Based on the selected metrics,we compare 9 node overlap removal algorithms. Our experiment involves 854 synthetic and real-world graphs. Finally, we propose a JavaScript library containing both the algorithms and the criteria, and we provide a Web platform, AGORA, in which one can upload graphs, apply the algorithms and compare/download the results.
(2)  We compare state-of-the-art node overlap removal approaches in regards to the previously selected metrics. Experiments involve 854 graphs, including synthetic ones (random, tree, scale-free, small-world) and real-world ones. (3)  We present a JavaScript library 2 , that contains all the algorithms described in this paper, and a Web platform, AGORA 3 (Automatic Graph Overlap Removal Algorithms), in which one can upload a set of graphs, apply the node overlap removal algorithms and download the results and the values of the quality criteria 4. The paper is organised as follows: after a brief reminder in Section 2 of the definitions and the notations used in this paper, we present and discuss the quality criteria and the metrics in Section 3. Then we compare the algorithms in Section 4. We discuss threats of validity and future research directions in Section 5.
Algorithms comparison In this section, we compare 9 algorithms of the literature in terms of quality and running time: uniform Scaling, PFS  [21] , PFS'  [13] , FTA  [16] , VPSC  [5] , PRISM  [8] , RWordle-L  [26] , GTREE  [22] , and Diamond  [20]. The quality of an overlapping-free embedding is evaluated with the metrics identified in the last section, by following a 3 steps procedure: 1. Datasets. We generate 840 synthetic graphs containing 10 to 1,000 nodes. These graphs are provided by 4 generation models available on the OGDF library  [4] : random graphs  [6] , random trees, small world graphs  [28] , and scale-free graphs  [2]. We also use 14 real-world graphs selected from the Graphviz test suite 5   [9] , previously used by the authors of PRISM  [8]  and GTREE  [22]. All the graphs are available online  6  as GML files including the initial embedding.
All the algorithms are coded in JavaScript. We implemented PFS, PFS', FTA and Diamond ourselves from the algorithms provided by the authors in their seminal papers. As Diamond is based on a linear optimization, we used the jsLPSolver 7. For VPSC, we directly used the JavaScript program provided by the authors  8. PRISM and GTREE have been adapted from the Microsoft Table  3 : Aggregated values of the selected metrics on the synthetic graphs: first quartile, median and third quartile. Automatic Graph Layout library 9  and converted into JavaScript with Sharp-Kit  10. Finally, we were inspired by the Java program of RWordle-L 11  provided by the authors of  [26].
Summary As a conclusion, even if Scaling optimises 4 out of 5 criteria and is very fast to compute on the graphs of our datasets, it does not represent a satisfying solution as it increases the size of the embedding too much. PFS is also not satisfying as it got poor results on 3 criteria. In particular, it also considerably increases the size of the embedding, which is obvious in Figures  1c  and 2c. FTA obtained intermediate results over all the criteria, which is less good than all its remaining competitors. In particular, as mentioned before, it spreads the embedding along only one axis, highly distorting the original configuration (see the Global Shape Preservation criterion gs bb iar in Table  3 , as well as the distortion illustrated by Figure  1e ). VPSC also holds this property. Looking at Table  3 , we can observe that RWordle-L is better than VPSC on Global Shape Preservation, while obtaining Table  5 : Aggregated running times in milliseconds on the synthetic graphs, function of number of nodes (10 to 1,000): first quartile, median and third quartile.
Threats to validity Nodes aspect ratio The aspect ratio of the synthetic graphs of the above study is 2:1 whereas the aspect ratios of the real-world graphs vary with respect to the initial datasets. The fixed aspect ratio of synthetic graphs could be considered as a limitation of our study: what happens in terms of result quality among the different algorithms when the aspect ratio varies, and in particular when the width of the nodes increases to display long text labels? A clue for answering this question is available in Table  7 , which shows the results for the graph of the Figure  1  with an aspect ratio of 2:1, and the same graph with an aspect ratio of 5:1. In this example, the metrics mostly highlight the same properties for both aspect ratios. The main differences appear on the global aspect ratio (gs bb iar) for PFS, FTA and VPSC.
Conclusion Finding a suitable node overlap removal algorithm is difficult for a visualisation designer because even if many algorithms exist, no complete comparison based on the same criteria has been provided. In this paper we first highlighted the five main classes of existing criteria and proposed a selection of one representative criterion for each class. Using a large number of experiments carried out with synthetic and real-world graphs, we compared 9 algorithms from the state-ofthe-art according to both criteria and running time. By analyzing the results, we then showed advantages, disadvantages and limitations of the algorithms, which can be very useful for the designer. Finally we proposed a Javascript library containing all node overlap removal algorithms and criteria as well as a Web platform, AGORA, that allows the end user to upload his/her own graphs and get the embeddings according to the selected algorithms.
* supported by the  LABEX MILYON  ( ANR-10-LABX-0070 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). † supported by the  ERC  Consolidator Grant 615640-ForEFront. The author acknowledges generous support from the  Netherlands Organisation for Scientific Research (NWO)  under project no. 016. Veni. 192. 250.
For example, a teacher can use SER to decide what subjects can be taught and must be able to develop strategies for managing emotions within the learning environment. That is why learner's emotional state should be considered in the classroom. Three key issues need to be addressed for successful SER system, namely, (1) choice of a good emotional speech database, (2) extracting effective features, and (3) designing reliable classifiers using machine learning algorithms. In fact, the emotional feature extraction is a main issue in the SER system. Many researchers  [4]  have proposed important speech features which contain emotion information, such as energy, pitch, formant frequency, Linear Prediction Cepstrum Coefficients (LPCC), Mel-frequency cepstrum coefficients (MFCC), and modulation spectral features (MSFs)  [5].
The Voiced Segment Selection (VSS) algorithm also proposed in  [20]  deals with the voiced signal segment as the texture image processing feature which is different from the traditional method. It uses the Log-Gabor filters to extract the voiced and unvoiced features from spectrogram to make the classification. In previous work  [21] , we present a system for the recognition of «seven acted emotional states (anger, disgust, fear, joy, sadness, and surprise)». To do that, we extracted the MFCC and MS features and used them to train three different machine learning paradigms (MLR, SVM, and RNN). We demonstrated that the combination of both features has a high accuracy above 94% on the Spanish database. All previously published works generally use the Berlin database. To our knowledge, the Spanish emotional database has never been used before.
For this reason, we have chosen to compare them. In this chapter, we concentrate to improve accuracy; more experiments have been performed. This chapter mainly makes the following contributions: • The effect of speaker normalization (SN) is also studied, which removes the mean of features and normalizes them to unit variance. Experiments are performed under a speaker-independent condition. • Additionally, a feature selection technique is assessed to obtain good features from the set of features extracted in  [21]. The rest of the chapter is organized as follows. In the next section, we start by introducing the nature of speech emotions. Section 3 describes features we extracted from a speech signal. A feature selection method and machine learning algorithms used for SER are presented. Section 4 reports on the databases we used and presents the simulation results obtained using different features and different machine learning (ML) paradigms.
Feature selection As reported by Aha and Bankert [34], the objective of feature selection in ML is to "reduce the number of features used to characterize a dataset so as to improve a learning algorithm's performance on a given task. " The objective will be the maximization of the classification accuracy in a specific task for a certain learning algorithm; as a collateral effect, the number of features to induce the final classification model will be reduced. Feature selection (FS) aims to choose a subset of the relevant features from the original ones according to certain relevance evaluation criterion, which usually leads to higher recognition accuracy  [35]. It can drastically reduce the running time of the learning algorithms. In this section, we present an  Process for computing the ST representation  [5].
Support vector machines (SVM) are an optimal margin classifier in machine learning. It is also used extensively in many studies that related to audio emotion recognition which can be found in  [10, 13, 14]. It can have a very good classification performance compared to other classifiers especially for limited training data  [11]. SVM theoretical background can be found in  [40]. A MATLAB toolbox implementing SVM is freely available in  [41]. A polynomial kernel is investigated in this work.
Experimental results and analysis 4. 1 Emotional speech databases The performance and robustness of the recognition systems will be easily affected if it is not well trained with a suitable database. Therefore, it is essential to have sufficient and suitable phrases in the database to train the emotion recognition system and subsequently evaluate its performance. There are three main types of databases: acted emotions, natural spontaneous emotions, and elicited emotions  [27, 44]. In this work, we used an acted emotion databases because they contain strong emotional expressions. The literature on speech emotion recognition  [45]  shows that the majority of studies have been conducted with emotional acted speech. In this section, we detailed the two emotional speech databases used for classifying discrete emotions in our experiments: Berlin Database and Spanish Database.
Berlin database The Berlin database  [46]  is widely used in emotional speech recognition. It contains 535 utterances spoken by 10 actors (5 female, 5 male) in 7 simulated emotions (anger, boredom, disgust, fear, joy, sadness, and neutral). This database was chosen for the following reasons: (i) the quality of its recording is very good, and (ii) it is public  [47]  and popular database of emotion recognition that is recommended in the literature  [19]. A basic concept of RNN and unfolding in time of the computation involved in its forward computation  [18].
Spanish database The INTER1SP Spanish emotional database contains utterances from two professional actors (one female and one male speaker). The Spanish corpus that we have the right to access (free for academic and research use)  [48]  was recorded twice in the «six basic emotions plus neutral (anger, sadness, joy, fear, disgust, surprise and neutral/normal)». Four additional neutral variations (soft, loud, slow, and fast) were recorded once. This is preferred to other created database because it is available for researchers use and it contains more data (6041 utterances in total). This paper has focused on only seven main emotions from the Spanish database in order to achieve a higher and more accurate rate of recognition and to make the comparison with the Berlin database detailed above.
Results and analysis In this section, experimentation results are presented and discussed. We report the recognition accuracy of using MLR, SVM, and RNN classifiers. Experimental evaluation is performed on the Berlin and Spanish databases. All classification results are obtained under tenfold cross-validation. Cross-validation is a common practice used in performance analysis that randomly partitions the data into N complementary subsets, with N À 1 of them used for training in each validation and the remaining one used for testing. The neural network structure used is a simple LSTM. It consists of two consecutive LSTM layers with hyperbolic tangent activation followed by two classification dense layers. Features from data are scaled to À1; 1 ½ before applying classifiers. Scaling features before recognition is important, because when a learning phase is fit on unscaled data, it is possible for large inputs to slow down the learning and convergence and in some cases prevent the used classifier from effectively learning for the classification problem.
The effect of speaker normalization (SN) step prior to recognition is investigated, and there are three different SN schemes that are defined in  [6]. SN is useful to compensate for the variations due to speaker diversity rather than the change of emotional state. We used in this section the SN scheme that has given the best results in  [6]. The features of each speaker are normalized with a mean of 0 and a standard deviation of 1. Tables  1 2 3 show the recognition rate for each combination of various features and classifiers based on Berlin and Spanish databases. These experiments use feature set without feature selection. As shown in Table  1 , SVM classifier yields better results above 81%, with feature combination of MFCC and MS for Berlin database. Our results have improved compared to previous results in  [21]  because we changed the SVM parameters for each type of features to develop a good model.
From Table  1 , it can be concluded that applying SN improves recognition results for Berlin database. But this is not the case for the Spanish database, as demonstrated in Tables  2  and 3. Results are the same with the three different classifiers. This can be explained by the number of speakers in each database. The Berlin database contains 10 different speakers, compared to the Spanish database that contains only two speakers and probably the language impact. As regarding the RNN method, we found that combining both types of features has the worst recognition rate for the Berlin database, as shown in Table  3. That is because the RNN model has too many parameters (155 coefficients in total) and a poor training data. This is the phenomena of overfitting. This is confirmed by the fact that when we reduced the number of features from 155 to 59 features, the results show an increase of above 13%, as shown in Table  4.
To investigate whether a smaller feature space leads to better recognition performance, we repeated all evaluations on the development set by applying a recursive feature elimination (LR-RFE) for each modality combination. The stability of RFE depends heavily on the type of model that is used for feature ranking at each iteration. In our case, we tested the RFE based on an SVM and regression models; we found that using linear regression provides more stable results. We observed from the previous results that the combination of the features gives the best results. So we applied LR-RFE feature selection only for this combination to improve accuracy. In this work, a total of 155 features were used;    The confusion matrix for the best recognition of emotions using MFCC and MS features with RNN based on Spanish database is shown in Table  5.
Conclusion In this current study, we presented an automatic speech emotion recognition (SER) system using three machine learning algorithms (MLR, SVM, and RNN) to classify seven emotions. Thus, two types of features (MFCC and MS) were extracted from two different acted databases (Berlin and Spanish databases), and a combination of these features was presented. In fact, we study how classifiers and features impact recognition accuracy of emotions in speech. A subset of highly discriminant features is selected. Feature selection techniques show that more information is not always good in machine learning applications. The machine learning models were trained and evaluated to recognize emotional states from   these features. SER reported the best recognition rate of 94% on the Spanish database using RNN classifier without speaker normalization (SN) and with feature selection (FS).
For Berlin database, all of the classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection (FS) are applied to the features. From this result, we can see that RNN often perform better with more data and it suffers from the problem of very long training times. Therefore, we concluded that the SVM and MLR models have a good potential for practical usage for limited data in comparison with RNN. Enhancement of the robustness of emotion recognition system is still possible by combining databases and by fusion of classifiers. The effect of training multiple emotion detectors can be investigated by fusing these into a single detection system. We aim also to use other feature selection methods because the quality of the feature selection affects the emotion recognition rate: a good emotion feature selection method can select features reflecting emotion state quickly.
Implementation We have built a user interface employing PyQt5 in Python 3. 6 to allow the user to visualize and interacting live with the data. The top panel of the main window displays the 1-dimensional original signal and bottom panel the 2-dimensional imaged-signal (figure  7 ). Graphical characteristics and window settings can be modified by the user using text boxes, combo boxes, toolbars, and menus on the top and sides of the window. Some possible actions are, for instance, panning, zooming, changing colors, moving along the x and y axes, or saving the figures. A user menu is also available for more sophisticated options such as creating a kernel for image convolution. Opening the kernel window (figure  8 ) allows the user to generate a Gaussian kernel with or without customizing its parameters (size and standard deviation).
User Feedback The only user of the GUI described is the first author of this paper, accustomed to analyzing EEG signals on different clinical or research software. The GUI was designed to visualize the original signal and its associated image to identify possible errors in the algorithm, which is still under development. The main idea being a proof of concept that density analysis of EEG can be useful, only one recording channel at a time is displayed, to avoid missing any important detail (but all channels are processed). Navigation over time is instantaneous, as is the navigation from one channel to another or the enabling and disabling of the convolution, allowing a fast and efficient interaction. An upcoming more sophisticated interface will be developed soon with the ability to visualize multiple channels at once and a more straightforward design so that the platform can be used instinctively by other users.
RESULTS We have evaluated the efficiency of EEG signal 2dimensional convolution to visualize and automatically detect interictal epileptic spikes (IESs). The data used are those of a patient who was hospitalized in 2015 and we know well. The EEG signal from the same patient were used in a previous study for which the purpose was different  (Despouy et al. , 2019). For this last study, the EEG signal was carefully examined and tagged. We ran the current tests on a 10 minutes part of this tagged signal and compared previous manual detection of IESs to automatic detection using our new method. This data set was composed of a hundred intracerebral macroelectrodes recorded at 2048 Hz that we downsampled at 512 Hz.
Furthermore, our tests have so far been carried out only on one patient implanted with a hundred intracerebral recording channels sampled at 512 Hz over 10 minutes. The first results lead us to be confident about the functionality of the technique and its potential performance, but we have to replicate them on a much more substantial amount of signal. This approach is currently underway with data recorded from more than 60 epileptic patients implanted for SEEG examinations between 2013 and 2019. In addition, the detection of other types of pathological markers such as high-frequency oscillations  (Bragin et al. , 2002; Bragin et al. , 1999b)  and more specifically fast-ripples  (Ibarz et al. , 2010; Zelmann et al. , 2009; Roehri et al. , 2017)  should be evaluated and compared with other detectors.
Data In the recent years, with the democratization of supervised machine learning techniques, several specialized corpora in English have been annotated with negation-related information, which has resulted in pre-trained models for automatic detection. These corpora can be divided into two categories: (1) corpora annotated with cues and scopes of negation, such as BioScope and * SEM-2012, and (2) corpora focusing on medical concepts/entities, such as i2b2 and MiPACQ. We briefly describe these corpora. The BioScope corpus  (Vincze et al. 2008 ) contains reports of radiological examinations, scientific articles as well as abstracts from biomedical articles. Available in the XML format, each sentence and each negation and uncertainty cue/scope pair receives a unique identifier. Table  1  provides some statistics about each subcorpus in BioScope.