dataesr/scientific-paragraphs-categorization huggingface dataset:
I. INTRODUCTION Determining similarities is part of a fundamental process of a human sense-making mechanism that consists of three elements: an object or event, a mental model, and an association between them  [1]. Similarities represent the core of that association scheme. The notion of similarity has been exploited in various fields of Computer Sciences  [2] -  [5]  such as in machine learning pattern recognition  [4] , classification  [6] , image processing  [7]  and decision making  [5]. The associate editor coordinating the review of this manuscript and approving it for publication was Juan Lara. Similarity in a machine learning context is required to compute the ''closeness'' between elements in a dataset. It allows to understand the structure within the input data  [8]. Moreover, it is widely used in classification tasks in order to find the best match between new instances and the already known ones (by training)  [9].
V. EXPERIMENTAL RESULTS This section presents results of an experimental investigation using four benchmark data sets extracted from the UCI repository  [37]  of machine learning databases. These databases are specifically dedicated to classification problems. The size of the databases is sufficiently large to infer a referent possibility distribution that we can consider as ground truth. • Base1: Banknote authentication data sets that contains 1372 instances of 10 features extracted from images of banknotes to decide whether the banknote is genuine or forged. • Base2: Page blocks classification data sets that is devoted to document analysis. It contains 5473 instances, each one describes a block of a document that can be identified according to the values of 10 attributes, in one out of 5 classes.
• Base3: Pima Indians diabetes data set that aims to predict diabetes disease referred on 768 individuals and based on eight factors. • Base4: Statlog data set (Vehicle Silhouettes) that seeks to classify a given silhouette from 946 instances as one of four vehicles types, using a set of 18 features extracted from the silhouette. To assess the performance of a similarity measure, we have to consider a large variety of conditions or factors that may affect the performance. These factors are: • Data quality: Degraded data quality may be due to high sensor noise, the interference from other signals during acquisition or other alterations that affect the data representativeness of the modeled sample. • The possibility distribution estimation approach: This influences directly the quality of the model and its ability to represent the entity.
A. DESCRIPTION OF THE EXPERIMENTAL METHODOLOGY Our objective is to compare the measures of similarity used within a possibilistic context. Seven similarity measures are considered for experimentation. They are listed in Table  4  according to the three main categories (morphic, amorphic and hybrid). The possibility distributions of the classes, according to one specific attribute, are being built from the four databases: Banknote authentication, Page blocks, Pima Indians diabetes, Statlog. These distributions are constructed using some wellknown approaches: the symmetric transformation of Dubois-Prade  [38] , the asymmetric transformation of Dubois-Prade  [39] , and the transformation of Klir  [40] ,  [41]. Two experiments are being conducted; identified below as Scenario-1 and Scenario-2.
1) SELECTION OF AN ATTRIBUTE FOR EACH DATABASE For each of the four databases, a unique attribute is being considered to model their respective classes. Considering the influence of the attribute on the evaluation of a similarity measure, we classify the attributes into three categories as follows: • Non-discriminative attributes: all the similarity measures give useless results. • Moderately discriminative attributes: an evaluation based on this type of attribute leads to a meaningful comparison between similarity measures. • Very discriminative attributes: enable the similarity measures to easily differentiate between the samples of each class. The selection step for the attributes has been conducted on each database using the Information Gain method  [42]  of the Weka application  [43].
Experiments have been conducted, for each similarity measure S and for the four databases, according to the following steps. • Step 1 : Different samples of the same size and belonging to the same class are extracted. Their respective possibility distributions are then matched based on the similarity measure S to a referent possibility distribution of their class. This is to build the genuine distribution (Fig. 2 ). Note that a good similarity measure should detect a high degree of similarity. than their own class. This is to build the imposter distribution (Fig. 3 ). Note that a good similarity measure should detect a low degree of similarity. A representation of the frequency of appearance of the similarity degrees in the two respective categories ''Similar'' and ''Dissimilar,'' on a scale of increasing similarity, is given in Figure  4.
3) SCENARIO-2 CLOSENESS OF POSSIBILITY DISTRIBUTIONS Scenario-2 aims at verifying if a similarity measure can reveal the closeness between distributions obtained from the same samples but estimated with different methods. Scenario-2 consists on extracting, repeatedly, 400 different samples of the same size from Diabetes database, and applying different methods on them to estimate possibility distributions. The predicted possibility distributions of each sample are then compared. We keep the average degree and the standard deviation of similarity obtained with each measure of similarity (Fig. 11 ). A good similarity measure has to give a high degree of similarity indicating that the compared distributions refer to the same sample. Also, a low standard deviation indicates that a similarity measure gives almost the same results during the evaluation process.
The three estimation methods used are listed as: 1) Symmetric transformation of Dubois-Prade  [38] ; 2) Asymmetric transformation of Dubois-Prade  [39] ; 3) Transformation of Klir  [41]. Fig. 12  illustrates three different methods to estimate the possibility distributions for the same sample. We note visually that these distributions seem to have a high degree of similarity since they have roughly a similar shape (curvature). Furthermore, they have a correspondence on the element having the maximum value. In this figure, we vary the sample size from 10 observations to 100 observations to visualize the proximity between possibility distributions with respect to the sample size. Fig. 13  illustrates, for the Diabetes database, the similarity average degree and the standard deviation, according to each considered sample size, and for the seven similarity measures.
VI. CONCLUSION In this paper, we have studied the similarity between possibility distributions. First, we have analysed a set of seven similarity measures and their required properties. These measures were classified into three main categories: morphic, amorphic and hybrid. Results show that some of the existing measures give inconsistent results in conditions of data incompleteness (e. g. small sample size). To circumvent that, we proposed a new hybrid similarity measure called InfoSpecificity that uses the variation of the amount of information between distributions combined with a standard metric distance. Experiments have been conducted both on numerical examples and on four experimental databases (UCLI). The results show the importance of hybridizing morphic and amorphic approaches such as in InfoSpecificity and InfoAffinity.
Constraints on experiment duration are severe with young children, and make it difficult to collect the same extensive datasets as in adults. A lack of sensitivity could also have arisen from the greater variance between children in terms of maturation, learning experience, attention and concentration on the task compared to adults who can remain attentive, perform more consistently, and are generally at the peak of the skill being tested (i. e. , no longer learning). Furthermore, the difference between typical and struggling readers may be smaller in children than in adults because even the normal children are still in the process of learning and are therefore not fully competent, whereas the adult typical readers (often recruited at university) are at ceiling. Finally, as has been shown in alphabetic languages, the slope of normal reading acquisition in children depends on the transparency of the writing system.
We did the same analyses in the same ROIs for the contrasts of faces vs fixation and houses vs fixation. The FDR corrected p value is denoted as pFDR_corr. Because some of our analyses evaluated the null hypothesis of no difference between two factors, we also performed Bayesian analyses on the same data, with the same factors as the ANOVA above, using the JASP software (https://jasp-stats. org). These analyses yield a Bayes Factor (denoted BF10), which estimates the likelihood ratio of the positive (1) over the null (0) hypothesis. A BF10 of 0. 20, for instance, indicates a five-times (1/0. 20) greater likelihood in favor of the null hypothesis and is equivalent to the inverse notation BF01=5.
Acknowledgements:  Perla Sousi  participated at an early stage of the project and we thank her for stimulating discussions, and her proof of Lemma 4. 1. We also thank  Quentin Berger  and  Julien Poisat  for the idea of considering the polymer melt. Finally, we thank two anonymous referees, whose suggestions were crucial in clarifying the arguments. The authors were partly supported by the  French Agence Nationale de la Recherche  under grants  ANR-17-CE40-0032  and  ANR-16-CE93-0003.
Acknowledgments. We wish to thank Stephen Muirhead and Alejandro Rivera for very fruitful discussions about randomized algorithms in the context of smooth Gaussian percolation. We also would like to thank Vincent Beffara, Charles-Edouard Bréhier, Damien Gayet and Avelio Sepúlveda for very interesting discussions. Moreover, we would like to thank Daniel Contreras Salinas, Stephen Muirhead and Alejandro Rivera for useful remarks on a preliminary version of this paper. Finally, we wish to thank an anonymous referee for helpful comments. This research has been partially supported by the ANR grant Liouville ANR-15-CE40-0013 and the ERC grant LiKo 676999.
A soil chemical quality index is designed for cacao production systems. It is based on worldwide scientific knowledge and built using data from three municipalities of Tolima department in Colombia. Fuzzy logic is used in a multicriteria decision making framework in two different ways. First, fuzzy sets are used to model an expert preference relation for each of the individual information sources to turn raw data into satisfaction degrees. Second, fuzzy rules are used to model the interaction between sources to aggregate the individual degrees into a global score. The whole framework is implemented as an open source software called GeoFIS. A part of the data was used for calibration, then the remaining data served as a validation set. The results were easy to analyze and in agreement with field observations.
Data combination is fully driven by expert knowledge and fuzzy logic is used at two different steps of the process. First, fuzzy sets are used to model an expert preference relation for each of the individual information sources according to the decision to be made. Second, fuzzy rules are used to model the interactions between sources to aggregate the individual degrees into a global score. The implementation is achieved by the means of an open source software, called GeoFIS 1  , a platform for spatial data processing with a decision support perspective. The design of the FIS is mainly automated. Only the rule conclusions, either real values in the unit interval or linguistic labels, are left to the user. This framework was used to manage another agronomic case study dealing the design of a nitrogen fertilization map within a vineyard  (Guillaume et al.
Implementation The fusion module is implemented as an open source software in the Ge-oFIS program. The data must be co-located, i. e. a record includes the spatial coordinates of the cell, from a pixel to a zone, and the corresponding attributes. For WAM and OWA the weights can be learned provided a target is available. Rule conclusions can also be learned using the FisPro software  (Guillaume & Charnomordic, 2011). Rule conclusion can be either a linguistic term, fuzzy output, or a real value, crisp output. Using a fuzzy output, it would be necessary to define as many labels as different suitable rule conclusions. As a crisp conclusion may take any value in the output range, it allows for more versatility. The output should also range in the unit interval. This constraint ensures the output can feed a further step of the process as shown in Figure  4.
S2 S1 In 2 In 1 In 3 Out 1 Out 2 Figure  4 : A hierarchical structure. This way, the intermediate systems can be kept small, making their design and interpretation easier. The GeoFIS program includes a distance function based on a fuzzy partition that allows for integrating expert knowledge into distance calculations  (Guillaume et al. , 2013)  as well other functionalities, such as a zone delineation algorithm  (Pedroso et al. , 2010). An illustration of its potential use in Precision Agriculture can be found in  Leroux et al. (2018). as guidance for characterizing and improving agricultural system sustainability, one of them is Soil Fertility. It is defined by the Soil Science Society of Amer-ica as 4  : "The quality of a soil that enables it to provide nutrients in adequate amounts and in proper balance for the growth of specified plants or crops.
Comparison with other aggregation operators The Soil Nutritional Balance component of the Chemical index is used in this section to investigate if an equivalent aggregation operator to the fuzzy system with the rule base shown in Table  2  can be designed. The output of the fuzzy system for the 10 farms from Chaparral is used to learn the weights of the WAM, OWA and Choquet Integral. The weights of the WAM and the OWA were learned using a least square minimization procedure under two constraints for the weights: they must be positive and their sum should be 1. The pnnls function from the lsei R package was used. In a preprocessing step, the degrees for each farm were sorted in an increasing order to learn the OWA weights. The data are given in Table  4. The degrees to aggregate are in the first three columns OM , pH and M acN ), followed by the output inferred by the fuzzy system (F IS).
In the general case fuzzy inference systems require a lot of parameters to define the input partitions and the inference operators. In the particular case of data aggregation all the input variable are satisfaction degrees with common scale and common meaning. This leads to a automatic setting of inputs using a strong fuzzy partition with two linguistic terms, Low and High. As a consequence, only the rule conclusions have to be specifically defined by the user. This is the way expert knowledge about variable interaction is modeled. This framework is implemented as an open source software called GeoFIS available at: https://www. geofis. org. This is a strong asset as software support availability is a key factor for a method to be adopted. A part of the available data, 10 farms, was used to tune and calibrate the system, the other 20 ones were used as a validation sample.
However, as it is directly impacted by intersubject and intersite variability, this approach hinders the fine demarcation of brain regions. Contrariwise, individual mapping is free from this variability  (Hanke et al. , 2014; Huth, de Heer, et al. , 2016) , but the resulting functional topographies have not yet been integrated into brain function templates. We thus investigate herein the feasibility of performing individual functional atlasing free from intersubject and intersite variability, as an effort to establish an univocal relationship between functional segregation of brain regions and human cognition. To this end, we leverage a collection of task-fMRI brain images from the Individual Brain Charting (IBC) dataset, acquired at high spatial resolution-i. e. 1. 5 mm-in a fixed cohort and environment.
We make use of the first IBC-dataset release  (Pinho et al. , 2018)  composed by twelve tasks that cover already a wide variety of cognitive systems. We describe an individual-brain-modeling approach, that benefits from high-resolution data, to better account for subject-specific functional organization. The main goal of this analytic approach is to reliably localize and extract regional-specific signatures from the tasks probed, at a fine-grained scale, and delineate neural territories based on such functional fingerprints. To understand the expected benefits of this approach, we first review the current practices for data integration and knowledge accumulation in cognitive neuroscience. Particularly, knowledge accumulation in the field relies not only on task-specific studies but also, and increasingly, on meta-analyses and mega-analyses as well as individual brain mapping.
, 2015; Gorgolewski, Varoquaux, et al. , 2016)  are opening novel perspectives regarding system-level cognitive studies. Nevertheless, it is still a challenge to mitigate heterogeneity in the data related to: (a) different acquisition settings; and (b) functional and anatomical intersubject variability. Recent neuroimaging studies have started to adopt individual analysis, in order to overcome both functional and anatomical intersubject variability  (Fedorenko et al. , 2011; Haxby et al. , 2011; Nieto-Castañón & Fedorenko, 2012; Frost & Goebel, 2012; Hanke et al. , 2014; Laumann et al. , 2015; Huth, de Heer, et al. , 2016; Huth, Lee, et al. , 2016; Braga & Buckner, 2017; Gordon et al. , 2017; Chang et al. , 2019). Specifically, previous neuroimaging-data initiatives have already triggered the development of large-scale datasets, laying the groundwork for a more comprehensive and systematic analysis of the organization of neural structures.
Much emphasis of the project was also given to characterizing population variability, in order to both conceive a common contrast atlas of the cohort  (Glasser et al. , 2016)  and investigate correlations with behavior (S. Smith et al. , 2015)  or genetics  (Kochunov et al. , 2015). As a precursor of the IBC dataset,  Pinel et al. (2007)  reported a standardized large-scale acquisition set (eighty one participants) of a fast event-related task-ARCHI Standard-that is also included in the IBC set of experiments. However, this study was dedicated to between-subject comparison rather than fine cognitive mapping. Part of the data are openly available in the Brainomics/Localizer database  (Orfanos et al. , 2017). This large-scale acquisition has now been extended to four localizers, all of which are used in the IBC dataset (i.
e. ARCHI Standard, ARCHI Spatial, ARCHI Social and ARCHI Emotional), and individual functional data, from seventy eight participants, make up the "CON-NECT/Archi" Database  (Pinel et al. , 2019 ). Yet, this dataset only accounts for four different tasks, comprising twenty three main-contrast maps. Worthy of note is also the studyforrest initiative. It stands for a group of openly available multimodal datasets stored in the OpenNeuro repository, featuring fMRI data on the continuous presentation of scenes included in the "Forrest Gump" movie. This project has thus launched several studies dedicated to investigating the neurocognitive encoding of complex auditory and visual information, like the ability to perceive language, music or social interplay, by modeling specific audio and visual properties of the stimuli  (Hanke et al.
, 2014 (Hanke et al. , , 2015 (Hanke et al. , , 2016;; Sengupta et al. , 2016). Nevertheless, the tasks employed across these studies were restricted to naturalistic stimuli. Hence, the ensuing results can not be easily integrated into current brain-function knowledge bases. The IBC dataset yields a comprehensive collection of individual contrasts that aims at characterizing the cognitive components underlying the tasks. The first release of this dataset  (Pinho et al. , 2018)  pertains mostly to data acquired from localizers, using a fast and randomized design, whose conditions range from perception to higher-order thinking skills  (Pinel et al. , 2007; Barch et al. , 2013). Besides, data from a rapid-serial-visual-presentation paradigm on language comprehension were also included in this first release  (Humphries et al.
To this end, we make use of the fifty-one main-contrast maps that can be extracted from the twelve tasks featuring the first release of the IBC dataset. These contrast maps are linearly independent and each contrast map refers to two contrasts, i. e. to the labeling contrast and its reverse. Concretely, these contrast maps pertain to unthresholded z-maps that are available in NeuroVault, collection #4438 (https://identifiers. org/neurovault. collection:4438). First, we performed a quality check of the IBC data to verify the reliability of the functional signatures measured in the contrast maps. We thus assessed whether contrast maps of those tasks taken from previous studies were successfully reproduced. Second, we extended this quality-checking investigation and studied the amount of variability across runs (with different phase-encoding directions) and across participants in these contrast maps.
| Participants The IBC dataset refers to neuroimaging data collected in thirteen individuals 3 (11 males, 2 females; age mean/SD=34±5 years) with no previous history of psychiatric, neurological or any other medical disorders that can change brain function. Handedness was determined with the Edinburgh Handedness Inventory  (Oldfield, 1971) ; results of the survey indicate that members of the cohort are predominantly right handed (range: [0. 3, 1]; mean∼0. 8). The experiments were carried out with the understanding and formal consent of the participants, in accordance with the Helsinki declaration and the french public health regulation. For more information about demographic data of the cohort, consult Table  A. 1 of the Appendix section A.
| Stimuli Stimuli consisted of both visual and auditory material presented to the participant during BOLD runs. For all tasks, they were delivered through custom-made scripts that allowed for a fully automated environment and computercontrolled collection of the behavioral data. Both visual and auditory stimuli of the protocols obtained from the HCP consortium  (Barch et al. , 2013)  were translated to french. All protocols are available in a public repository: https://github. com/hbp-brain-charting/public_protocols.
| MRI Equipment The fMRI data were acquired using an MRI scanner Siemens 3T Magnetom Prisma fit and a Siemens Head/Neck 64channel coil. Behavioral responses of the participants were registered during the MRI sessions with one of the two MRcompatible, optic-fiber response devices, depending on the protocol: (a) a five-button ergonomic pad (Current Designs, Package 932 with Pyka HHSC-1x5-N4); and (b) a pair of in-house custom-made sticks featuring one-top button. MR-Confon package was used as audio system in the MRI environment. All sessions were conducted at the NeuroSpin platform of the CEA Research Institute, Saclay, France.
| Experimental Procedure The task-fMRI data of the IBC-dataset first release were collected per participant in four different MRI sessions. Upon arrival to the research center, participants were instructed about the execution and timing of the tasks referring to the upcoming session. Every session was always composed of several runs dedicated to one or more tasks and each task-related run was always repeated in multiples of two, alternating its phase-encoding direction (see Section 2. 5 for details). Full information on the structure of the MRI sessions, namely which tasks were acquired in every session, can be found in Sections 2. 3 Experimental Procedure and 2. 5 Data Acquisition and Tables  2  and 3  of  Pinho et al. (2018)  as well as in the Section 2 Acquisition parameters of the IBC documentation (see Section 2.