dataesr/scientific-paragraphs-categorization huggingface dataset:
METHODOLOGY This work is based on fieldwork, using elicitation and playback methods. We collected our data from three native signers, who regularly collaborate with us, a man and two women all aged between 29 and 40 years. Before starting data collection, informants were told that we were interested in the documentation of possible sentences in LSF. They had no precise knowledge of our research questions and goals. The elicitation was conducted in two major steps: picture-based elicited production 12  and a syntactic analysis. All the data produced by our informants were video recorded, transcribed, and glossed. The first step of the elicitation consisted in asking for the description of paired pictures. We asked the informants to produce a sentence for each picture, and, only after, to sign a complex sentence in which the two events were temporally related.
When informants agreed to sign an alternative, we recorded it and made them judge it in another session, along with all the other sentences. This methodology allowed us to record more positive data and to add, even more importantly, some negative evidence. Following the "playback method" (Schlenker 2010), every utterance obtained in the recording session was played back in a different session and rated again by all the informants. This procedure was applied to make sure that the data obtained could be generalized. For each sentence, informants had to give a judgment on a seven-point scale regarding two different aspects: acceptability (i. e. , does the sentence seem natural in LSF?) and felicity (i. e. , does the sentence describe the picture accurately?). We only present the sentences that were judged accordingly across all informants.
,n in the Φ(z) basis. 3. Sort the basis coefficients (α k (x)) k=1,. ,K in the decreasing order of the criterion  (14). We denote (k), k = 1,. , K, the indices of the coefficients following the new order. Select the K K most important coefficients such as  14 )). 4. Apply PCA in R K on the dataset of coefficients evaluated at design points (α(x (i) )) i=1,. ,n. K k=1 λ (k) ≤ p (Equation ( Choose the first n P C principal components. Denote t 1 (x (i) ),. , t n P C (x (i) ), i = 1,. , n, the coordinates on the first principal components, and w 1 ,. , w n P C , the associated eigenvectors. 5. For each principal component l = 1,. , n P C , predict t l (x ) (denoted tl (x )) by GP regression (see  (11) ), based on the observation t l x (i) ) (i = 1,. , n). 6. Predict the coefficients α k (x ).
An analytical test case In this section, GP metamodelling using standard PCA, FPCA based on wavelet basis, FPCA based on B-splines basis (respectively denoted GP PCA , GP FPCA wavelet , and GP FPCA B-splines ) are applied on an analytical case (presented in Section 5. 1). Then, section 5. 2 explains how wavelet and B-spline basis are defined. The optimal parametrization of FPCA for metamodelling procedure is selected using a cross-validation method (see section 5. 2). Next, the comparison of all methods is made (Section 5. 3). Finally, generalized sensitivity indices are implemented using simulations obtained by FPCA-based method. All implementations are performed using the statistical programming language R  [26]. A R package, named GpOutput2D  [27] , has been developed. GpOutput2D contains functions for applying FPCA and GP regression modelling on two-dimensional functional data.
Prediction accuracy The three metamodelling methods presented in section 5 are also compared in the coastal flooding case. The dataset of simulation results contains 253 flooded maps, and 247 maps without any flooding (i. e. all water depths are at zeros). Metamodels have been trained using a learning dataset of n learning = 400 maps. Half of them have been randomly chosen among runs for which flooding has occurred (the other half thus corresponding to maps without any flooding). In order to test the metamodels' prediction accuracy, we use the remaining n test = 100 ones as test samples. The settings of FPCA-based methods have been done as in Section 5. We have chosen D4 Daubechies wavelets with one level of resolution. We have used B-splines of degree 1, with 100 knots equally spaced on each dimension of the spatial domain.
Acknowledgement We are grateful to an associate editor and two anonymous reviewers for their relevant comments, that led to substantial improvements. The project is funded by the French geological survey BRGM and the French state reinsurance company CCR. The authors thank for their feedbacks the participants of the Chair in Applied Mathematics OQUAIDO, gathering partners in technological research (BRGM, CEA, IFPEN, IRSN, Safran, Storengy) and academia (CNRS, Ecole Centrale de Lyon, Mines Saint-Etienne, University of Grenoble, University of Nice, University of Toulouse) around advanced methods for Computer Experiments.
This evaluation, described in detail in Balaguer et al. [27] , used a visual analog scale from 0 (severe impairment) to 10 (normal speech). The speaker's score was obtained by averaging the scores from 6 speech therapists considered to be experts on speech disorders. An intra-class correlation coefficient was calculated to assess the interjudge reliability of this subjective evaluation. The high degree of agreement between the jury's scores (r = 0. 77) demonstrated that the jury was homogeneous and could act as a gold standard  [28]. The patients' data showed severity scores between 0. 58 (severe impairment) and 9. 7 (normal speech) with a mean at 6. 2 and a third quartile boundary at 8. 0. Twenty-five percent of the patients received scores higher than 8/10, indicating only mild speech impairments (Fig.
Perception Task We recruited 40 native French-speaking everyday listeners with no former experience with a speech disorder and without any hearing impairments to carry out the perception task. Our choice of everyday listeners was based on our desire to create an "ecological" situation in which all listeners are considered specialists in their language and therefore capable of carrying out the task of phonetic decoding without any specific medical or auditory expertise. In other words, this choice was motivated by the fact that the everyday listeners represent "typical communication partners"  [29]. Forty listeners transcribed the productions in the corpus using the LANCELOT software program  [30]. They received the following instructions: "You will hear a series of non-words. A non-word is a combination of sounds from the French language which has no meaning (e.
Correlation with the Clinical Measurement of Severity As described previously, we have a severity index of the disorder for each speaker on an ordinal scale of 0 (severe impairment) to 10 (normal speech). In order to verify the concurrent validity of the PPD test, we examined the correlation between the PPD score and the clinical judgment of severity. These two coefficients were well correlated with an R spearman equal to -0. 85. This correlation was obtained using data from only 102 speakers because the dataset had 15 missing values for the clinical measurement of severity (see Missing Data and Outliers).
Within the framework of the ANR-18-CE45-0008 Rugbi Project (https://anr. fr/Project-ANR-18-CE45-0008), we are also studying acoustic-phonetic decoding by examining the effect of linguistic factors on the perceptual identification of intervocalic consonants in a reading task. The PPD score used was computed on single consonants taken from the continuous speech recorded in a reading task. The results are being examined as a function of consonant nature, oral/nasal vocalic context, word class (function or content), and prosodic position within sentences  [37]. Within the same project, we are also comparing the PPD score with the results of automatic speech analysis techniques as described in Laaridh et al. [38]  and Abderrazek et al. [39] , which will contribute to the test's validity. In terms of implications for clinical practice, we are developing an application that will allow our test to be administered during a consultation.
Acknowledgements The authors thank the CEP staff (www. lpl-aix. fr/∼cep),especially  Carine André , for assisting in the perceptual experiments. The authors thank  Oriana Reid Collins  (Collins Traduction, https://collinstraduction. com/) for her English proofreading work in the text. Testing Intelligibility through  Acoustic-Phonetic Decoding of Pseudowords 13 Folia Phoniatr Logop DOI :  10. 1159/000519427.
Motivated by the successes of DL in different areas, numerous DL-based AMR techniques have been proposed based on CNN, RNN, DNN and other network architectures in recent years and they have been observed to achieve significant results  [1]. Jdid et al. [1]  presented a comprehensive survey on existing AMR methods developed with different machine learning (ML) and DL models. They compared these ML-based and DL-based AMR methods in terms of their network structures, training and testing hyperparameters, noise conditions, modulation pools and performances. O'Shea and West  [2]  employed GNU Radio to generate a synthetic dataset known as the RadioML 2016. 10A that contains eleven types of digital and analog modulations. A new AMR method was developed by O'Shea and Corgan  [3]  by using CNN and its performance was investigated with the dataset proposed in  [2].
O'Shea et al. [4]  amended the tool described in  [2]  to generate RadioML 2018. 01A and more details of this new dataset will be summarized in Section II-B. Their previous works  [3] ,  [5] ,  [6]  with adaptation of CNNs for AMR were extended to solve the new dataset, where in-depth investigation in terms of the design parameters, channel impairment and training dataset parameters were studied through various simulations including over-the-air measurement of AMR performance. Another CNN-based AMR was proposed by Zhang et al. [7]  by employing the Short-Time Fourier Transform (STFT) to convert the input signal spectrograms into image representation. Similarly, Sun et al. [8]  adopted an image dataset with 10 different modulation schemes to build a CNN-based AMR from a popular deep learning model known as VGG-16  [9].
Zhang et al. [10] , applied both of Wigner-Ville and Born-Jordan distributions to convert the received signals into two types of images. These converted images were then fed into two CNNs for feature extraction before fusing them with the selected HCFs in order to solve the classification problems. The work reported by Peng et al. [11]  exploited the adaptation of constellation diagrams and a popular CNN model called AlexNet  [12]  for AMR. Similarly, constellation diagrams were employed as the inputs of feature extractor in  [13] , whereas a support vector machine (SVM) was used as the classifier for AMR. Given that the qualities of datasets are crucial to improve the performance of AMR, the pre-processing technique such as data augmentation was employed by Tang et al. [14]  to ensure sufficient data were available to construct the datasets required for the training and testing of DL-based AMRs.
Particularly, constellation diagrams were converted into the contour stellar images in  [14]  to obtain more color features than those of  [11]. Several issues encountered in training process such as the divergence of generator, overfitting of discriminator and mode collapse can degrade the overall performance of traditional generative adversarial nets  [15]. Numerous measures were proposed to address the poor performance issues of GANs and simulation studies revealed that the classification accuracies can be improved through the extension of datasets. Most existing FB-AMR approaches proposed in earlier era  [16]  were designed based on the extraction of HCFs. Meanwhile, the DL-based AMR approaches proposed in recent literatures were found to heavily rely on the ideas of using deep neural network architecture for feature extraction  [17].
Extensive amounts of previous research works in image processing have shown the advantages of concatenating the contextual features (CFs) and HCFs  [18] -  [20]. In particular, a proper combination between HCFs with CFs is able to achieve the performance boosting via more diverse representation of features. To the best of authors' knowledge, limited investigations were performed to search for the optimal combinations of CFs and HCFs. This is because the quality of CFs is governed by the efficiency and abundance of the used datasets. Meanwhile, the value of each HCF may change with different signal-to-noise ratio (SNR) values. It is also noteworthy that some HCFs tend to have the same values for different types of modulations. These undesirable characteristics of HCFs tend to jeopardize the accuracies of classifiers through the distortion of their hyperplanes.
In order to address these technical drawbacks, it is crucial to design an efficient feature selection algorithm that is able to build the robust feature set. As mentioned earlier, the performances of DL-based AMR methods are governed by the qualities of datasets, hence the latter should be constructed carefully to include a varsity of real-life effects. While the larger number of training examples can lead to better performance of DL-based AMR methods, the involved training processes tend to be time-consuming. This issue may be resolved by splitting the original large dataset into multiple subsets of data for the training process. The hyperplanes of classifiers also tend to be distorted when dealing with datasets that have very wide ranges of SNR. Although training a classifier with the wide range of SNR can eliminate the requirements of SNR estimation, CNNs with highly complex network structures are required to extract the relevant features.
The outline of this paper is summarized as follows. The background of AMR and specifications of RadioML 2018. 01A dataset considered in current study are presented in Section II. Other required information such as the signal parameters and channel characteristics are also covered in this section. Section III explains the algorithm used to extract and select an optimal subset of HCFs, whereas the extraction process of CFs using CNNs are elaborated in Section IV. The overall mechanisms of proposed DL-based AMR method are then explained in Section V and the performance comparisons with baseline methods are presented in Section VI. Finally, the concluding remarks of current study are presented in Section VII.
II. AMR BACKGROUND AMR is an essential process used to identify the type of modulation scheme adopted by received signals before recovering these received information through the demodulation process. In this section, a brief background of FB-AMR is first presented. Then, we wrap up this section by providing an overview of the most popular signals dataset, RadioML 2018. 01A [4] , in AMR field. A. FB-AMR BACKGROUND Fig. 1  shows the general block diagram of conventional FB-AMR methods that consist of three main phases: • Pre-processing phase: Several variables such as Carrier Frequency Offset (CFO), baud rate, Phase Offset (PO), SNR and timing offsets can be obtained from the input signals via different pre-processing techniques. The quality of received signal can be enhanced in this phase before proceeding to feature extraction phase.
B. SIGNALS DATASET RadioML 2018. 01A The RadioML 2018. 01A dataset proposed by O'Shea et al. [4]  is considered in this study because it is one of the most challenging datasets for AMR problem. This dataset consists of two different compositions of classes spread across a wide range of SNR values. The ''Normal'' classes of RadioML 2018. 01A dataset consists of 11 modulation schemes commonly seen in the impaired environments, whereas the ''Difficult'' classes contain the 24 digital and analog modulation schemes. It is also notable that the RadioML 2018. 01A dataset contains more than 2. 5 million frames of modulation signals along with different synthetic simulated channel effects such as CFO, symbol rate offsets, delay spread and thermal noise. In this paper, the performance of proposed DL-based AMR method will be evaluated using the ''Normal'' classes.
1) SNR SPLITTER In practical, SVM classifiers can provide good computation speed and memory, and work relatively well when there is a clear margin of separation between few classes. Therefore, a fast linear SVM classifier is adopted as the SNR splitter of proposed DL-AMR method. For each signal obtained from the dataset, a vector of HCFs denoted as G 1 is extracted. In offline training, all HCFs vectors that are extracted from the signals with SNR values lesser than a predefined threshold (Thr) are labeled as L 1 , whereas those with SNR values greater than Thr are labeled as L 2. After the SVM is trained, it will be integrated in the proposed AMR method as the SNR splitter. It will identify the range of SNR value of the received signals before HCFs and CFs extraction system. The complete pseudo-code of SNR splitter is presented using Algorithm 1.
The second derivative of each CV k vector is hence calculated to determine the SNR range where the CV of an HCF starts to vary. In principle, the positive and negative values of second derivative imply that a curve starts to concave up and concave down, respectively. The SNR range can be detected when a feature value starts to change drastically with increasing rate of variation along with the concave upward trends of curve. Table  3  shows the CV km values of each k-th HCF for all groups of S m , where the highlighted cells indicate the CV k of particular feature is in a concave upward trend during S m. Notably, two consecutive groups of CV km-1 and CV km should be selected to obtain a center value of SNR as the required threshold. In order to fulfill the objectives of SNR splitting, it is also desirable to define a threshold value that is able to split the signal dataset into two subsets with similar sizes.
3) EVALUATION OF PROPOSED SNR-SPLITTING ALGORITHM The proposed SNR-splitting algorithm is evaluated on both ''Normal'' and ''Difficult'' classes of adopted dataset. Tables  4  and 5  show some potential splitting thresholds obtained by the proposed algorithm together with their corresponding selected HCFs and splitting accuracy for both ''Normal'' and ''Difficult'' classes, respectively. For ''Normal'' class, Thr = + 2 dB is identified as the best SNR splitting threshold that can produce the selected HCFs of G 1 with the highest splitting accuracy of 99. 49%. G 1 = {P 5 , P 6 , P 7 , σ aa , m A , E} (34) Fig. 3  shows the variation of mean envelope for each selected HCF in G 1 along with the overall SNR range for all modulation schemes considered in the ''Normal'' class. Each selected HCF of modulation schemes are observed to have different values around the splitting threshold of Thr = + 2 dB that divide the total SNR range into two smaller ranges.
A. SIMULATION SETTINGS RadioML 2018. 01A dataset is selected for performance evaluation. The performances of proposed HCFs selection algorithm and proposed DL-based AMR method are compared with the methods used by O'Shea et al. [4]. The parameter settings required for simulation studies are presented in Table  11. The method introduced by Wu et al. [30]  to select a robust set of HCFs based on the normalized variance is considered for performance validation of the proposed HCFs selection algorithm. In addition, a Baseline (BL) AMR method presented in  [4]  is also selected for AMR performance validation of the proposed AMR based on the proposed HCFs selection algorithm and SNR splitter, where the former method considers a total of 28 HCFs including HOMs, HOCs and other features. Finally, both of the CNN/VGG and RN methods introduced in  [4]  are also considered for performance validation of the proposed DL-based AMR method.
Cross Entropy (CE) function is applied as loss where y c is the ground truth vector that can be achieved through one-hot encoding of sample label. y c is the predicted vector. C is the number of the samples' types. Table  15  shows some key hyper-parameters for the training process of both CNN 1 and CNN 2. The mini batch size of 1024 with 30 epochs are set for the training processes of both CNN models. In addition, a stochastic gradient descent with momentum (SGDM) optimizer is chosen to prevent the overfitting of networks. After constructing both CNN 1 and CNN 2 according to Table  14 , their weights are initialized with learning rate equal to 0. 01. Thereafter, CNN 1 and CNN 2 are trained on the corresponding training dataset and their weights are updated according to SGDM until validation loss is not improved in setting epochs.
Let us illustrate the interest of these properties with an application that creates a multi-party privacy preserving deep learning model  [6]. In their solution, participants (i. e. service providers) compute local models on private datasets, and the system (i. e. the customer) aggregates the models' parameters, and not the private data, to create a more accurate model. Our contributions, which address all these constraints, are the following: 1) Fair Service Delegation: We propose a protocol where customers delegate services to service providers; 2) Proofs of fairness: We show that our solution is fair, i. e. a transaction either succeeds, or ends without any party gaining an unfair advantage over the other, and that we have no possible disputes.
Large datasets increasingly provide critical insights into crustal and surface processes on Earth. These data come in the form of published and contributed observations, which often include associated metadata. Even in the best-case scenario of a carefully curated dataset, it may be nontrivial to extract meaningful analyses from such compilations, and choices made with respect to filtering, resampling, and averaging can affect the resulting trends and any interpretation(s) thereof. As a result, a thorough understanding of how to digest, process, and analyze large data compilations is required. Here, we present a generalizable workflow developed using the Sedimentary Geochemistry and Paleoenvironments Project database. We demonstrate the effects of filtering and weighted resampling on Al 2 O 3 and U contents, two representative geochemical components of interest in sedimentary geochemistry (one major and one trace element, respectively).
INTRODUCTION The study of Earth's past relies on a record that is spatially and temporally variable and, by some metrics, woefully undersampled. Through every geochemical analysis, fossil identification, and measured stratigraphic section, Earth scientists continuously add to this historical record. Compilations of such observations can illuminate global trends through time, providing researchers with crucial insights into our planet's geological and biological evolution. These compilations can vary in size and scope, from hundreds of manually curated entries in a spreadsheet to millions of records stored in software databases. The latter form is exemplified by databases such as The Paleobiology Database (PBDB;  Peters and McClennen, 2016) , Macrostrat  (Peters et al. , 2018) , EarthChem  (Walker et al.
, 2005) , Georoc  (Sarbas, 2008) , and the Sedimentary Geochemistry and Paleoenvironments Project (SGP, this study). Of course, large amounts of data are not new to the Earth sciences, and, with respect to volume, many Earth history and geochemistry compilations are small in comparison to the datasets used in other subdisciplines, including seismology (e. g. ,  Nolet, 2012) , climate science (e. g. ,  Faghmous and Kumar, 2014) , and hydrology (e. g. ,  Chen and Wang, 2018). As a result, many Earth history compilations likely do not meet the criteria to be called "big data," which is a term that describes very large amounts of information that accumulate rapidly and which are heterogeneous and unstructured in form  (Gandomi and Haider, 2015;  or "if it fits in memory, it is small data"). That said, the tens of thousands to millions of entries present in such datasets do represent a new frontier for those interested in our planet's past.