dataesr/scientific-paragraphs-categorization huggingface dataset:
When applying NoN and Fuzzy logic ideas to Brazilian biodiversity monitoring, several practical issues and challenges will be faced, such as the quality of empirical data, the integration of expert opinions, the system's own credibility and the linkage to management decisions. These challenges notwithstanding, with the rapid development of online databases, selection of indicators and demands for integrated systems, we urge moving from multiple disparate networks to a NoN. We believe that the development of global biodiversity monitoring schemes can go a step further than simply visualizing trends of each indicator individually in a common dashboard of indicators and instead be an integrated, analytically-linked tool with a wide global view of complex biodiversity indicators. Our point is not so much the need to develop completely new frameworks toward a "network of networks biomonitoring systems" but rather that the first step is to connect the frameworks and ongoing initiatives in the same platform.
Acknowledgments We thank  Dr Toby Gardner  for helping us with some initial ideas. This work was partially supported by  Biota-MS Program ,  CNPq ,  CAPES  (FVN was supported by  PNPD-CAPES  Scholarship # 1671210 ),  CAPES-Cofecub  (CAM was supported by  CAPES-Cofecub  #  88887. 190652/2018-00 ),CASEST, Fundect, and  PELD-Serra da Bodoquena. We thank for Institutional Program of Internationalization sponsored by  Coordination for the Improvement of Higher Education Personnel  (Capes-Print number  41/2017 ).
72, SD:. 07 ; body mass (kg) M: 80, SD: 12; and body mass index (kg/m 2 ) M: 26. 9, SD: 4. 2 kg/m 2 of body mass index). Due to poor gaze recording quality (see Data Recording and Analysis), we had to exclude two participants in the young group from the study. All participants had normal or corrected-to-normal vision (self-reported and examined by Snellen chart eye acuity test). The participants were all familiar to computer work and used their right hand to work with the computer mouse. The handedness was also confirmed by the Edinburgh Handedness Inventory  [64]  self-assessment measure, i. e. , a mean laterality index (LI) of 74. The participants were asked to abstain from alcohol for 24 h and caffeine for 12 h prior to each experimental session. The participants reported a habitual 6-10 h of sleep per night and slept at least 6 h (mean 7.
B. THE EXPERIMENTAL TASK: FUNCTIONAL COMPUTER WORK We customized a graphical user interface (GUI) (WAME 1. 0) in a MATLAB R2015b environment to implement the computer task framework. The task was designed based on a standard model of computer work which has been used in previous studies  [67] -  [69]  and involved cyclic computer operations in which each cycle was organized in sequential periods, namely memorization (MP), washout (WP), and replication periods (RP). The work panel contained two sections, i. e. , a template panel with a textual instruction and a replication panel (Supplementary Materials, Fig  1 ). The GUI was displayed on a 19-in screen (1280×1024 pixels, refresh rate: 120Hz) located approx. 56 cm in front of the participant. The GUI subtended approx. 27 • of the visual angle horizontally and approx.
D. DATA RECORDING AND ANALYSIS 1) APPARATUS A video-based monocular eye tracker (Eye-Trac 7, Applied Science Laboratories, Bedford, MA, USA) was utilized to track eye movements at a sampling frequency of 360 Hz during the experimental sessions. Head-mounted systems are known to be the most accurate and versatile eye tracking solutions  [28]. Head movements were also tracked in 3D using a motion tracker (Visualeyez II system set up with two VZ4000 trackers, Phoenix Technologies Inc. , Canada). The data from the eye tracker and motion tracker were coupled to compensate for head movements and to precisely estimate the point of gaze (built-in functionality of Eye-Trac 7). An example of the trajectory of the eye gaze on the screen while performing the task is depicted in Fig 1. The calibration of the eye tracker was performed before starting the tasks with a 9-point calibration protocol.
We further exploit multiple modalities and models in the SSL system, by using collaborative SSL, where all modalities and models are considered simultaneously; samples are selected by means of minimising the joint entropy. This strategy is supposed to not only improve the performance of the model for data annotation and consequently enhance the trustability of the automatically labelled data, but also to elevate the diversity of selected data. To evaluate the effectiveness of the proposed approaches, we performed extensive experiments on the RECOLA database, which includes multimodal recordings of spontaneous affective interactions of dyads. The empirical results show that the proposed approaches significantly outperform recently well-established SSL methods.
In  [11] , Schels et al. employed a density estimation of all available data to transfer the label information to unlabelled data. Similar work was further reported in  [12] , but for the text-based emotion classification. In contrast to these transductive SSL approaches where both labelled and unlabelled data are considered to perform a prediction on the unlabelled data, more research efforts need to follow an inductive SSL paradigm, mainly due to the fact that the powerful capability of discriminative models (e. g. , Neural Networks) for emotion recognition has been frequently shown over the past decade  [13]. In the inductive paradigm, a predictive model is pre-built only on the labelled data and then used for predicting the unlabelled data. As an example, Zhang et al. [14]  employed a typical inductive SSL approach called self-training to explore the unlabelled data from different databases for emotion recognition from speech.
It is supposed to correct possibly mislabeled data in previous iterative learning stages and this further enhances the overall confidence of the system predictions. • We demonstrate the superiority of the proposed ecSSL approach on a multimodal database and provide insightful analysis. The remainder of this article is organised as follows. In Section II, we describe the proposed enhanced collaborative SSL in detail. Then, we perform extensive empirical evaluations on the RECOLA database in Section III. Finally, we draw conclusions and point out some potential research directions in Section IV.
C. Modality-based Collaborative SSL The proposed collaborative SSL (cSSL) in this article can be considered an extension of co-training, where the views involve not only the feature domains (i. e. , modality-based cSSL), but also the recognition models (i. e. , model-based cSSL, discussed in Section II-D). When integrated with the enhanced SSL, the new algorithm is named as enhanced cSSL. The pseudocode describing the algorithm of enhanced cSSL based on multimodality is displayed in Algorithm 2. Compared with self-training, modality-based cSSL (e. g. , audio, video, text, and physiology) employs multiple modalities as independent 'views' for training different models. Compared with cotraining, it can implement multiple, rather than two, modalities in the learning system, which is similar to multi-view learning with less restriction in terms of conditional independence (For more details, the reader is referred to  [42] ).
Besides, in contrast to conventional co-training where different views individually select the samples that are classified with lowest entropies and then fuse them together (i. e. , minimum-individual-entropy strategy)  [15] ,  [41] , cSSL takes a minimum-joint-entropy strategy. That is, all predictions obtained by various views for each sample will be merged as one by means of majority voting. Particularly, in the even cases, the final decision is assigned to the category classified with the least entropy. This algorithm improvement can not only avoid the prediction-conflict caused by different views but also potentially increase the automated annotation correctness of the selected data  [43]. Furthermore, the final entropy is calculated by averaging all entropies obtained by different views.
D. Model-based Collaborative SSL In contrast to modality-based cSSL, the model-based cSSL seeks the benefits from multiple diverse classifiers, which are trained on the same feature sets. The pseudocode of its enhanced approach is shown in Algorithm 2 as well. When combining multiple models (classifiers) into a strong one, it normally requires the individual ones to be sufficiently effective and diverse  [44]. Again, for the sake of simplicity, we choose two models for evaluation in this article (i. e. , Q = 2 in Algorithm 2). The two models are Support Vector Machines (SVM) and Recurrent Neural Networks (RNN), each of which are widely applied to emotion recognition  [13] ,  [23] ,  [38]. In detail, SVM is a convex optimisation function, the characteristics of which offer it the capability to capture the global optimisation.
E. Enhanced Collaborative SSL based on Multi-Modality and -Model An enhanced cSSL based on multi-modality and -model is illustrated in Fig. 1 , which integrates the enhanced modalitybased cSSL (cf. Section II-C) and the enhanced model-based cSSL (cf. Section II-D). By this approach, the data from the audio and video domains are respectively utilised to build RNN and SVM models. For each sample, predictions via various modalities and models are merged to one by majority voting y x = M (y 1 x1 , • • • , y q xp , • • • , y Q x P ), (2) where y q xp denotes the prediction from the q-th model by using the p-th modality. In case of a draw, the decision is then made by the category that holds the least entropy. Meanwhile, the joint prediction entropy is calculated by Ē(y x ) = 1 Q • P Q q=1 P p=1 E(y q xp ), (3) where E(•) indicates the prediction entropy.
A. RECOLA Database The multimodal corpus REmote COLlaborative and Affective interactions (RECOLA)  [46]  (the standard database of the AVEC challenges for audiovisual emotion recognition in 2015 and 2016  [29] ) was selected for our experiments due to its widespread use in this area. This database was created to study socio-affective behaviours from multimodal data in the context of remote collaborative tasks. Spontaneous and natural interactions were proceeded from 46 French-speaking participants (27 females and 19 males with a mean age of 22 years and a standard deviation of 3 years) whilst solving a collaborative task conducted in dyads via video conferencing. In total, the database includes 9. 5 hours multimodal recordings, i. e. , audio, video, electrocardiogram, and electro-dermal activity, which were obtained synchronously and continuously over time.
Due to the consent of the participants to share their data, the data set is reduced to a subset of 34 participants with an overall duration of 7. 0 hours. After the data collection process, six gender-balanced French-speaking assistants were asked for annotating the timecontinuous ratings of emotional arousal for the first five minutes of all recordings via the ANNEMO web-based annotation toolkit. For the purpose of this study, these continuous ratings for arousal dimension are further discretised into a binary category -POSitive and NEGative. To do this, the continuous audiovisual signals were firstly split into sequential short segments (instances) via voice activity detection. Then, we assigned POS or NEG to each of them if the average rating value of the segment is above or under zero.
However, we obverse that the models cannot always achieve the highest UARs throughout all experimental scenarios, for example, 74. 8 % and 70. 3 % of UARs were obtained by using RNN or SVM as classifiers, respectively, for the audio+video modality, which are lower than the best results delivered by multimodality or multi-modal based SSL. These exceptions possibly attribute to the limited sample number of the database we employed for experiments. Despite of this observation, it can be seen that the fused approach outperforms the ones based on either multi-modality or multi-model for audio, video, or audio+video modalities in four out of six cases when using RNN or SVM. Therefore, the fused multi-modality & -model based SSL is particularly attractive when without knowing which modality or model fits the data best.
IV. CONCLUSION To leverage the ubiquitous unlabelled data for automatic emotion recognition, this article proposed enhanced collaborative Semi-Supervised Learning (SSL). Dissimilar to traditional SSL, it performs a data re-evaluation process on previously selected data (enhanced strategy) on one hand. On the other hand it takes a mutual learning process among multiple modalities and models (collaborative strategy). The proposed approaches have been systematically evaluated on the widely used audiovisual affective database RECOLA in various settings. The experimental results demonstrate that the proposed approaches significantly improve the system performance by enhancing the correctness and diversity of selected data. More recently, deep learning algorithms have attracted tremendous attention and achieved a great success in the context of machine learning.
Conclusions The investigation of PA, energy expenditure and sedentary measurements in an overfeeding context shows the use of various tools as well as a technological advance putting forward their uses in laboratory but also in free-living context. This systematic review draw a good state of the literature, as it is performed in eight databases and followed the PRISMA guidelines. Adaptations, both increase and decrease, seemed to occur in PA parameters following overfeeding. This might have been influenced by duration of the overfeeding protocols. An interesting consideration may be an "overfeeding % x duration protocol" factor for a global view of the overfeeding protocol. Unfortunately, 5 studies included in this review gave a specific amount of energy intake in excess instead of an overfeeding percentage, thus making it impossible to compare its effects on energy expenditure, PA and sedentary parameters.
Specificities of the Paradigm In the standard computer vision paradigm, the problem of object identification is generally tackled in a supervised way by training a representation learning algorithm, for instance Deep Convolutional Neural Networks  (Gu et al. , 2015). These algorithms are trained on a large database of static images containing objects, where the identity of the object is provided as a label (see for instance the well-known ImageNet database  Deng et al. , 2009). Labeling such databases requires a large human effort which can be mitigated by using semi-supervised or transfer learning approaches, without fundamentally changing the underlying object perception paradigm. In this paradigm, identifying an object consists in extracting from a collection of static images of the same object some invariant set of visual features which are sufficient for discriminating this object from any other.
From an engineering point of view, this paradigm is quite efficient as it provides a working solution for many concrete applications. From a more fundamental standpoint, it captures some important aspects of perception in terms of invariant visual features which are not captured in our work. But this paradigm goes with some issues, as revealed for instance by failure on adversarial examples  (Szegedy et al. , 2013). Another well-known issue is that relying on external labels makes the agent limited to the recognition of objects present in the database. In that respect, using unsupervised learning methods is mandatory if one wishes to design a truly autonomous learning agent. Our work reveals a third issue. Indeed, any approach processing static images individually cannot extract any object from our simulations since the distribution of pixel values is the same in proto-objects and in the environment.
Body parameterization In this study, a digitalized human body was prepared to serve as a personalized visual mannequin, with which the design and construction of the personalized garment can be realized  [22]. First, a 3D scanning operation was performed to get the raw data, which is a set of points forms the shape of the body surface of the scanned human body. The scanning operation was performed using 3D scanning machine and corresponding software from Human Solution company. Next, scanned data from 3D scanning, which consists of a three-dimensional point cloud of the object surface was then meshed into small facets, using the meshing tool of the Rapidform software. The meshed human body model was then exported into Lectra Design Concept software, which is able to edit 3D objects. The software chosen here are of commercial type, which are wildly used in fashion industry.
2D pattern generation The meshed garment block surface can then be applied to generate the corresponding 2D patterns using the flattening operation. The generation of flattened 2D patterns also strictly follows the principle classical 2D pattern design knowledge. Darts, folds, opening, fabric direction (Warp and Weft direction) and other important 2D pattern design elements should be fully considered. Then the meshed virtual garment surface is divided into 4 parts: right front, left front, right back and left back (Figure  6-b ). 8 different surfaces are generated: 4 in the  In Modaris software, virtual fabric can be simulated based on the parameters that of the real fabric. Using the simulated virtual fabric, a virtual try-on can be ensured to validate the design idea. There is a database of the virtual fabric, which contains common fabrics widely used in fashion industry.
In this research, parameters of the selected fabric is shown in Table  1. Using these parameters, the virtual try-on with desired fabric can be realized. Figure  8  shows the flattened 2D patterns and the 3D virtual try-on in Modaris software. It should be noted that, the 2D patterns have already linked with the 3D virtual try-on. The 3D virtual try-on can display the modification and adjustment of the 2D patterns automatically. Table  1  presents the fabric parameters in the virtual try-on procedure. After that, in order to make sure that the flattened 2D garment pattern can be applicable for industrial application, several modifications were performed following 2D pattern design knowledge. Curves of the patterns are reshaped to be smooth. Corresponding curves pairs are carefully checked to ensure the same length and in right position.
Evaluation and adjustment The visual evaluation analysis of the proposed garment block pattern is carried out using the colorization tools of CAD software (Modaris and Lectra Design Concept) to evaluate garment pieces. Colorization can be done on several patterns of a garment selected during the activation of these tools, see Figure  8. A scale of colors is associated with each type of colorization, which can be described in Table  2. 1. Fabric parameters in the virtual try-on procedure.
Cognitive performance is highly heritable. However, little is known about common genetic influences on cognitive ability and brain activation when engaged in a cognitive task. The Human Connectome Project (HCP) offers a unique opportunity to study this shared genetic etiology with an extended pedigree of 785 individuals. To investigate this common genetic origin, we took advantage of the HCP dataset, which includes both language and mathematics activation tasks. Using the HCP multimodal parcellation, we identified areals in which interindividual functional MRI (fMRI) activation variance was significantly explained by genetics. Then, we performed bivariate genetic analyses between the neural activations and behavioral scores, corresponding to the fMRI task accuracies, fluid intelligence, working memory and language performance.
Imaging-genetics resources, such as the Human Connectome Project (HCP) provide an unprecedented opportunity to study the variability of such endophenotypes in control subjects, as well as to determine their potential heritability or association with genetics. Following up on these ideas, we first proposed to study the additive genetic variance involved in fMRI activation differences among typically developed individuals. We used the pedigree data from the HCP language comprehension and verbal math fMRI tasks. These tasks recruit regions directly implicated in brain disorders, such as Broca's area in SLI  8  , the angular gyrus in developmental dyslexia  1  and the intraparietal in dyscalculia  2. A few studies have already attempted to estimate the narrow sense heritability of brain activations for various tasks.
Subjects This study utilized the dataset of the Human Connectome Project (HCP). The HCP scans and data were released in April 2017 (humanconnectome. org). The details of the release are available in the HCP reference manual. In this project, 1046 subjects aged between 22 and 37 years old (µ ± σ = 28. 8 ± 3. 7 years) completed the fMRI language task in the HCP S1200 release. To avoid population stratification, we only included the 785 Caucasian individuals (372/413 M/F) that were classified as race="White" by the HCP; among these, the ethnicity of 69 was "Hispanic/Latino". This subgroup of the HCP contains 178 twin pairs (117 monozygotic twins (MZ) with 103 siblings and 61 dizygotic twins (DZ) with 61 siblings and 1 half sibling), 203 siblings, 1 half sibling and 60 unpaired individuals. The unpaired individuals did not contribute to the genetic parameter estimation but allowed a more accurate estimation of mean and variance effects.
Subjects were chosen by the HCP consortium to represent healthy adults beyond the age of major neurodevelopmental changes and before the onset of neurodegenerative changes  53. They underwent a battery of tests to determine if they met the inclusion/exclusion criteria of the HCP  53. All subjects provided written informed consent on forms approved by the Institutional Review Board of Washington University. All of the following methods were carried out in accordance with relevant guidelines and regulations. All experimental MRI protocols were approved by the Institutional Review Board of Washington University. Data availability statement: HCP (https://db. humanconnectome. org) is a publicly available dataset. Investigators need to apply to be granted access to restricted data.
06 and 0. 08 for phenotypes with heritability between 0. 20 and 0. 45  57, 58. Using the GCTA Power Calculator  55  with the default value for the variance of the single nucleotide polymorphism (SNP) derived genetic relationship matrix of 10 -5 , this range of standard errors and heritability estimates requires a sample size of 6000-7000 unrelated individuals. To allow comparison with other pedigrees studies, we computed a relatedness summary parameter (RSP) using the pedRSP R package  59. We determined that our sample of 785 individuals is equivalent to an effective number of 1,922 pairs of siblings (RSP of our sample: V λ = 0. 649 , V q = 0. 671, γ = 0. 73; see  59  for details of the parameter's meaning). Additionally, we want to emphasize that modeling non-additive genetic effects  60  is not possible when genetic data are not available.
Image acquisition and processing MR images were acquired using a 3T Connectome Scanner, adapted from Siemens Skyra, housed at Washington University in St Louis, using a 32-channel head coil. T1-weighted images with 256 slices per slab were acquired with the three-dimensional magnetization-prepared rapid gradient echo (3D-MPRAGE) sequence: TR=2400 ms, TE=2. 14 ms, TI=1000 ms, flip angle=8°, FOV=224×224 mm, and resolution 0. 7 mm isotropic. T2-weighted images, 256 slices per slab, were acquired with a 3D-T2SPACE sequence: TR=3200 ms, TE=565 ms, variable flip angle, FOV=224×224 mm, and resolution 0. 7 mm isotropic. fMRI data acquisition parameters were as follows: TR=720 ms, TE=33. 1 ms, flip angle=52 deg, BW=2290 Hz/Px, in-plane FOV=208×180 mm, 72 slices, and 2. 0 mm isotropic voxels, with a multi-band acceleration factor of 8.