dataesr/scientific-paragraphs-categorization huggingface dataset:
In S4A project, the entities of interest are cultivated fields and, thus, the S4A smart app back-end associates the agronomic categories of the BBCH ontology specified in VGI items to the agronomic parcels surveyed and stored in the agronomic cadastral database, which is provided as the conflation data layer. VGI items outside the fields are dealt with as outliers and stored in a different layer (Figure  4  shows some fields tagged with VGI). The administrator can change the conflation data layer to customize the framework to different projects and needs: all VGI items with geofootprint inside the boundary of an entity of the conflation data layer are associated with that specific entity. In the first version of the S4A smart app, the VGI geofootprints which were far from any entity, were associated with a null entity.
Based on user feedback, we developed a second version of the app that associates these VGI items with more entities to distinct degrees computed by exploiting knowledge of both the imprecision of the GPS localization and camera orientation, detected when creating the VGI items. This contextual information is associated with the VGI item when it is created and is stored in the geodatabase together with VGI geofootprint: specifically we store the associated GPS imprecision radius and the azimuth angle of the camera orientation detected when the volunteer has taken the picture, and thus when the camera frames the target parcel. The degree of association of a VGI item to a cadastral parcel can be computed inversely proportional to the distance of the VGI geofootprint.
Based on user feedback, we developed a second version of the app that associates these VGI items with more entities to distinct degrees computed by exploiting knowledge of both the imprecision of the GPS localization and camera orientation, detected when creating the VGI items. This contextual information is associated with the VGI item when it is created and is stored in the geodatabase together with VGI geofootprint: specifically we store the associated GPS imprecision radius and the azimuth angle of the camera orientation detected when the volunteer has taken the picture, and thus when the camera frames the target parcel. The degree of association of a VGI item to a cadastral parcel can be computed inversely proportional to the distance of the VGI geofootprint that intersects the parcel boundary in the direction of the camera.
A Web GIS server deploys the VGI items stored into the geodatabase as distinct views on the Web, together with the conflation data layer and, possibly, other multisource geospatial data of interest to the project via OGC Web Services. A metadata record, based on INSPIRE and the Italian National regulation "Repertorio Nazionale dei Dati Territoriali", is created for each type of VGI layer with basic information describing the common characteristics of its VGI items, i. e. , category type, link to the URL of the used extensive BBCH ontology, common keywords to empower data discovery, and the bounding box containing all VGI geofootprints. This way the users of VGI have all extensive information on the semantic and can thus interpret correctly the meaning of VGI. To this end, the S4A geoportal allows accessing the created VGI together with other geo-data of the project.
Lessons Learned within the Space4Agri Project During the vegetation season of 2015 and 2016, the S4A smart app has been used by farmers, agronomists, students of an agronomic high school and S4A researchers to collect and document the A Web GIS server deploys the VGI items stored into the geodatabase as distinct views on the Web, together with the conflation data layer and, possibly, other multisource geospatial data of interest to the project via OGC Web Services. A metadata record, based on INSPIRE and the Italian National regulation "Repertorio Nazionale dei Dati Territoriali", is created for each type of VGI layer with basic information describing the common characteristics of its VGI items, i. e. , category type, link to the URL of the used extensive BBCH ontology, common keywords to empower data discovery, and the bounding box containing all VGI geofootprints.
• The VGI consumers, when analyzing the VGI collection created based on fuzzy ontologies, can express flexible queries constraining the VGI items' defects to be below a desired value, so as to retrieve ranked VGI items as in fuzzy databases  [27]. They can select only the crop observations having at least a minimum level of possibility to be the true development stages of the observed crops, as computed by applying uncertainty level-based approximate reasoning. To definitely assess the advantages/drawbacks of the approach we need to carry out a user evaluation. We are now implementing a new version of the S4A smart app using the fuzzy BBCH scale and allowing users to specify the amount of defect of their observations. This is the prerequisite for carrying out a real on field experimentation aimed at evaluating the benefits of using the fuzzy OWL ontologies coupled with uncertainty level-based approximate reasoning.
Inventive Design Method: IDM The Inventive Design Method (IDM)  [3] [4] [5]  was developed to solve classical TRIZ (Theory of Inventive Problem Solving)  [6, 7]  limits and consequently address wider and more complex problematic situations specifically in the concept generation stage. The four major steps of IDM are: 1) Analysis of the initial situation, 2) Contradiction formulation, 3) Synthesis of Solution Concepts, 4) Choice of Solution Concepts to develop. In the third step, the key components of the contradictions are used as input to generate Solution Concepts assisted by computer-based TRIZ techniques. This framework has already been published and has been developed into a software prototype called STEPS (Systematic Tool for Efficient Problem Solving)  [8]. The main components of each Solution Concept are: 1) A description template, which describes an abstract context, general properties, performance functions and a Model of Problem, and 2) A sketch of the Solution Concept, which is synthesized from a Model of Solution, hypotheses and a technical systems' laws of evolution.
Several authors  [17, 18]  proposed applying the risk/failure in the early stage of design. The relation of componentfunction-failure mode/rate  [19]  is identified and populated as a database. However, the application is still limited to the conceptual design of a system. Technology Readiness Levels (TRLs) are a systematic metric/measurement system that supports assessments of the maturity of a particular technology and the consistent comparison of maturity between different types of technology. The System Readiness Level (SRL)  [20]  is an improved version of TRL. SRL is a multiplication between TRL of a system and the Integration Readiness Level (IRL) of systems. The application of SRL is relied on the system design.
5 There is sufficient control between objects necessary to establish, manage, and terminate the integration. The Solution Concept Readiness Level is determined via (1). In parallel, the Function-Failure Mode/Rate of the new objects and relations is determined via  (2). The snippet of database used as reference is presented in Fig. 3. (1) (2) This approach is resumed with the next Solution Concept to be considered. After all Solution Concepts have been tested, the ranking and selecting process will be implemented. This approach is resumed with the next Solution Concept to be considered. After all Solution Concepts have been tested, the ranking and selecting process will be implemented.
Multivariate pattern analysis (MVPA) has been applied successfully to task-based and resting-based fMRI recordings to investigate which neural markers distinguish individuals with autistic spectrum disorders (ASD) from controls. While most studies have focused on brain connectivity during resting state episodes and regions of interest approaches (ROI), a wealth of task-based fMRI datasets have been acquired in these populations in the last decade. This calls for techniques that can leverage information not only from a single dataset, but from several existing datasets that might share some common features and biomarkers. We propose a fully data-driven (voxel-based) approach that we apply to two different fMRI experiments with social stimuli (faces and bodies). The method, based on Support Vector Machines (SVMs) and Recursive Feature Elimination (RFE), is first trained for each experiment independently and each output is then combined to obtain a final classification output.
Second, this RFE output is used to determine which voxels are most often selected for classification to generate maps of significant discriminative activity. Finally, to further explore the clinical validity of the approach, we correlate phenotypic information with obtained classifier scores. The results reveal good classification accuracy (range between 69% and 92. 3%). Moreover, we were able to identify discriminative activity patterns pertaining to the social brain without relying on a priori ROI definitions. Finally, social motivation was the only dimension which correlated with classifier scores, suggesting that it is the main dimension captured by the classifiers. Altogether, we believe that the present RFE method proves to be efficient and may help identifying relevant biomarkers by taking advantage of acquired task-based fMRI datasets in psychiatric populations.
, 2001 (Haxby et al. , , 2014)). Applied to psychiatry, MVPA is a promising method to detect brain states that discriminate patients from controls and thus constitutes a valuable tool to identify potential biomarkers  (Mourão-Miranda et al. , 2005; Pereira et al. , 2009). In recent years, MVPA has indeed successfully been used on fMRI data to classify patients with major depression  (Fu et al. , 2008)  or drug addiction  (Zhang et al. , 2011)  with accuracy rates ranging from 70% to 80%. However, given the wealth of existing fMRI datasets collected in psychiatry research, there is a need for techniques that can go beyond the analysis of single datasets and that allow researchers to leverage information from multiple datasets at once. Such methods would increase biomarker sensitivity and allow us to make the most of existing data.
While there are obvious benefits in reanalyzing large datasets, the absence of efficient methods to merge information across datasets makes the process quite labor-intensive. Existing methods such as non-parametric permutation tests  (Nichols and Holmes, 2001)  and searchlight methods  (Kriegeskorte et al. , 2006 ) cannot be directly applied to mine information from several experiments, or when the assumption of sample independence does not hold. This rationale applies particularly well to Autism Spectrum Disorders (ASD), where large corpuses of brain data have been collected and, for a good fraction of them, made publicly available. Autism is a neurodevelopmental disorder characterized by a unique profile of impaired social interaction and communication which takes the form of an inadequate appreciation and modulation of behavior according to socio-emotional information  (World Health Organization, 1992).
While restricting classification to a limited set of ROI is a laudable conservative approach, combining information from multiple fMRI datasets may help to improve the detection reliability of relevant biomarkers, in particular those having a small spatial extent. This goal requires a data-driven approach that can mine information from the entire brain at the voxel-wise level. The goal of this paper is to propose a multivariate method that can combine information from several studies to detect activity patterns at the voxel-wise level which are significantly predictive of autism. We used data from two distinct experiments acquired in the same group of ASD and control participants. As in Coutanche et al. 's study  (Coutanche et al. , 2011) , the tasks we used were initially designed for univariate analysis and were not planned with MVPA in mind.
Both tasks required to process emotional stimuli under different conditions of social relevance or feature-based attention: the first experiment investigated the perception of angry or neutral faces with direct or averted gaze (adapted from  Conty et al. , 2012) ; the second experiment required participants to direct attention to or away from angry and neutral body expressions (adapted from  Pichon et al. , 2012). We report two analyses on these datasets. In Analysis 1, we estimated the classifier's ability to discriminate patients from controls after training the classifier on both studies. In Analysis 2, we extended this diagnosis-based approach to assess whether this classifier is correlated with individual differences in social motivation, a dimension of behavior that likely plays an important role in social deficits observed in ASD.
fMRI data acquisition For both experiments, gradient-echo T2*-weighted transverse echoplanar images (EPI) with BOLD contrast and a high-resolution T1weighted anatomical image were acquired with a 3T. Each volume contained 40 axial slices (TR/TE/Flip angle = 2000 ms/27 ms/78°, resolution = 64 * 64, voxels size 3 * 3 mm with 3 mm thickness, Parallel Acquisition Technique (PAT) factor 2). A shimming procedure minimized inhomogeneity of the static magnetic field. Image acquisition started after the recording of three dummy volumes to avoid T1 saturation. For each subject and for each task, we collected 370 functional volumes. In addition, we collected a high-resolution T1-weighted anatomical image (TR/TI/TE/Flip angle = 2300 ms/ms/4. 18 ms/9°, resolution = 256 * 256 * 64, voxels size 1 × 1 mm with 1 mm thickness, no IPAT acceleration, 176 sagittal slices).
Method Our first goal was to train classifiers to discriminate patients from controls and to evaluate the classifier's performance. The proposed classification method took into account the specificity of our dataset, which consists of non-uniformly distributed beta maps generated from different tasks and experiments. Secondly, feature selection was applied to improve classification performance and to identify the most discriminative voxels across the two experiments. We used the beta maps of each condition estimated at the individual step level as inputs for the classification to verify the hypothesis that fMRI brain activity discriminates ASD individuals from the controls. The space formed by the beta map voxels was directly used as the feature space for classification, without including any prior information such as a priori ROIs.
SVM training and leave-one-participant out cross-validation method This method was employed to test the performance of the classification method on unseen data. For each participant and each condition, a classifier was trained using the data of all other participants (on the same condition); the obtained model was then applied on the beta map of the tested participant. As described above average fusion was performed to combine the models of both experiments and obtain a final decision per participant. Finally performance was computed. Two types of fusion were employed by combining different conditions together to assess: 1. whether the combination of all models (i. e. both experiments) improved the classification accuracy; the performance obtained from combining the conditions of all experiments (i.
Classification performance The classification performance was measured using accuracy (percentage of correctly classified participants), sensitivity (i. e. recall) and specificity. The significance of the classification accuracy was tested using a binomial test with the null hypothesis that the class labels are estimated randomly and equiprobably. Given that Binomial tests can be too lenient when applied on small datasets  (Noirhomme et al. , 2014) , we also performed permutation tests. The two tests were found to be very similar and only the Binomial tests are presented.
(  2 ) by an average: This method allows finding voxels which are discriminative among all conditions and experiments by selecting voxels where pðZ 0 ≥z 0 Þb α N. In our study α was set to 0. 05. Note that the proposed modification does not change the statistical significance of the test since p(Z′ ≥ z ') = p(Z ≥ z). z 0 ¼ - X k i¼1 log r i N þ 1 k Z 0 Γ k; 1 k : However, the proposed test is not informative of the direction of the effect. To circumvent this issue, we retrieved voxels' weight signs by training a unique SVM model on the full dataset and we displayed in Fig. 3  the z statistics (which are always positive) multiplied by the sign of the associated weights to visualize the main direction of the effect. Anatomical labeling was performed with reference to the anatomy toolbox  (Eickhoff et al.
e. they derived from different tasks and experiments); 3) we relied on relatively small samples lying in a highly dimensional space, which might have raised a curse of dimensionality issue. In response to this last challenge, our results indicate that feature selection with SVM RFE reduces the impact of the curse of dimensionality by selecting discriminant subsets of voxels. In response to the second challenge, the proposed fusion approach allowed to build a model for each task and condition thus solving the problem of non-identically distributed samples. Taken together, the present study confirms that classifiers can be successfully applied to mine information from multiple BOLD datasets without relying on a priori ROIs and even if they are not originally designed for multivariate pattern analysis.
In contrast to this dimensional approach past studies have often focused on overall diagnosis. Anderson et al. , (2011)  and  Coutanche et al (2011) , for instance, found that their classifier scores correlated with ADOS total scores. Similarly,  Deshpande et al. (2013)  found that top rank features of connectivity measures were positively correlated with autistic traits' scores (AQ). In line with the RDoC framework (NIMH Research Domain Criteria), we would like to highlight that looking at relevant dimensions of behavior instead of overall diagnosis is a promising approach to understand the biological roots of ASD and, ultimately, to identify biomarkers. In conclusion, the present study indicates that RFE is an interesting method to leverage information from several datasets and explore potential brain atypicalities in ASD or other psychiatric conditions (e.
Acknowledgments The authors would like to acknowledge  Prof. Dimitri Van De Ville  and  Dr. Djalel Eddine Meskaldji  for their helpful comments and insightful suggestions on some of the methodological aspects of this work. This work was supported by grants from the  French National Research Agency   ANR-11-EMCO-00902 ,  ANR-11-0001-02   PSL *,  ANR-10-LABX-0087 , by the  Fondation ROGER DE SPOELBERCH, Fondation pour la Recherche Psychiatrie & Santé Mentale  and by  INSERM. Guillaume Chanel  is supported by the  National Center of Competence in Research (NCCR) Affective  sciences financed by the  Swiss National Science Foundation  (no. 51NF40_104897 ) and hosted by the  University of Geneva. Swann Pichon  is supported by an  Ambizione  starting grant of the  Swiss National Science Foundation  (no.
I. INTRODUCTION The ISTEX project compiles and provides access to scientific publications to French research. ISTEX-R project aims to highlight evolution of research topics using the ISTEX database. To that aim, we designed a new theoretical framework to track scientific fields changes and similarities across time : appearing and disappearing topics, splitting and merging topics. This framework is based on diachronic analysis, which aims to compare data or results from two distinct periods. Furthermore, this framework was implemented as a toolbox called Diachronic'Explorer that integrates all diachronic analysis steps and provides efficient visualizations. In the context of our framework, we basically consider tp time periods, containing documents split into separate sets according to these time periods and described by a set of features F (words or expressions) : D = ∪ 0≤i≤tp D i.
For each time period, clustering algorithms detect cluster sets such as CS = ∪ 0≤i≤tp C i. Each set of clusters C i = {c 1 , • • • , c ni } is constituted of n i = |C i | clusters describing data D i. Clusters gather similar documents and thus represent topics. Consequently, to monitor topic changes, our framework aims to track cluster changes and similarities across time periods. The first step of our new framework consists in labeling clusters of every cluster sets. These labels will then be used to track cluster changes and similarities. Our labeling step consists in extracting prevalent features sets S cj for each cluster c j ∈ C i of all the cluster sets C i ∈ CS using a feature selection method. We implement the feature maximization method proposed by Lamirel et al. [3]  that has proven to be efficient for both supervised and unsupervised learning.
The Feature F-Measure F F cj (f ) is the harmonic mean of these two values. The Feature F-Measure is thus used to select features that are discriminant and representative for each cluster c j using Equation  3. Sc j = f ∈ F |F Fc j (f ) > F FC i (f ), F Fc j (f ) > F FD i (3) with F F (f ) the mean F-Measure value of f across clusters of C i where f is non-null, and F F Di the mean F-Measure across all features and all data of the considered period. Figure  1  shows how features selected for a cluster with Diachronic'Explorer are used as labels to describe the cluster in the visualization module.
IV. EXPERIMENTAL RESULTS AND VISUALIZATIONS Experiments and visualizations shown in this papers were conducted on a corpus constituted of bibliographic data from the ISTEX project  [6]. The ISTEX database is queried to extract papers related to research in medical care between years 1996 and 2010. This results in a dataset of 9779 papers. Because the goal is to observe evolution of this field across time, a community detection algorithm  [5]  exploiting the relationship between documents indexes and the publication years is used to extract meta-periods. Obtained meta-periods are  1996-2000, 2000-2005, 2006 -2010  [1]. GNG clustering  [2]  is launched several times on the data of each meta-period to extract an optimal number of clusters with the help of adhoc quality indexes  [4]. Then, using Diachronic'Explorer 1 , we Fig.
3. Exploring cluster labels of a cluster set using Diachronic'Explorer. Each column/color describes a period where rectangles stands for labels clusters. Grey flows between the colored rectangles represent different kind of matchs (asymmetric/symmetric) and their strength. produce and then visually explore the diachronic results with powerful dynamic visualizations using advanced Javascript 2. Visualization tool of Diachronic'Explorer makes possible to navigate easily from one granularity level to another : cluster and their labels (Figure  1 ), match between two periods (Figure  2 ), overall dataset evolution (Figure  3 ).
During the first phase, the robot learns the task, but also records all the images. Consequently, a database is created to perform offline processing. Each image was annotated with the response of the robot during the online learning. All the images are correctly labeled because the participant mimics the robot's postures. After the mutual imitation encounter, we presented for a second time each partner through a set of pictures for testing recognition (see below). To show the generalizability of our sensory-motor approach when the robot is immersed in different learning contexts, three different experiments were carried out (Fig. 2 ) by changing either populations (e. g. children with ASD or avatars), robotic platforms (e. g. complete robot or Robot head), or motor abilities (e. g. facial motor expression or body posture).