dataesr/scientific-paragraphs-categorization huggingface dataset:
Probabilistic and reliability analysis The application of Direct Monte Carlo Method (DMCM) to the PCEbased metamodels allows direct access to the Cumulative Density Functions (CDF) of the various outputs of interest (preview of available results expressed in CVs are provided in Table  4 ). * quasi-constant over time during the operational phase ** Evolution between the 1st and last pressurization tests *** Data acquired from 24 strain gauges and 24 thermal sensors positioned at the gusset's intrados and extrados within a cover distance of 5 cm. In the particular case of the air leakage rate through the VeRCoRs mock-up, the obtained results are depicted in Fig. 13  for the air flow rate through concrete mass, through early age cracks and through the whole structure. One should note that, at the stage of this study, available results are limited for the first 4 pressurization tests (Test 0 to 3) corresponding to the first 3 years of the operational lifespan of the VeRCoRs mock-up  (2015) (2016) (2017) (2018).
Acknowledgements This work was supported by  EDF-SEPTEN/DTG/CIH  within the Chair PERENITI agreement with the  Grenoble INP Partnership Foundation  and by the joint MACENA and  ENDE  project fund (ProjetIA- 11-RSNR-0009  and  ProjetIA-11-RSNR-0012 ). The author is grateful to EDF-SEPTEN for the provided in situ measurements. The Chair PERENITI partners shall not in any circumstances be deemed liable for the content of this publication which is only binding its author. The  3SR  lab is part of the  LabEx Tec 21  ( Investissements d'Avenir -Grant  agreement  n°ANR-11-LABX-0030 ).
If a denominator is zero, that fraction is discarded. From the implementation point of view, PCR6 is simpler to implement than PCR5. For convenience, very basic (not optimized) Matlab TM codes of PCR5 and PCR6 fusion rules can be found in  [12] ,  [13]  and from the toolboxes repository on the web  [14]. The main drawback of PCR5 and PCR6 rules is their very high combinatorial complexity when the number of source is big, as well as the cardinality of the FoD. In this case, PCR5 or PCR6 rules cannot be used directly because of memory overflow. Even for combining BBAs defined on a simple dichotomous FoD as those involved in the Inter-Criteria Analysis (ICrA), the computational time for combining more than 10 sources can take several hours 2. That is why a fast fusion method to combine dichotomous BBAs is necessary, and we present it in the next subsections.
The explicit expression of x and y are difficult to obtain analytically (even with modern symbolic computing systems like Mathematica TM , or Maple TM ) because one has a quartic equation to solve whose general analytical expression of its solutions is very complicate. Fortunately, the solutions can be easily calculated numerically by these computing systems, and even with Matlab TM system (thanks to the fsolve function) as soon as the numerical values are committed to a and to b, and this is what we use in our simulations.
V. APPLICATION OF THE FAST BF-ICRA METHOD In this section we present the results of the fast BF-ICrA method with the MO-ACO algorithm for WSN layout deployment. Fidanova and Roeva have developed a software, which realizes the MO-ACO algorithm. This software can solve the problem at any rectangular area, the communication and the coverage radius can be different and can have any positive value. We can have regions in the area. The program was written in C language, and the tests were run on computer with an Intel Pentium 2. 8GHz processor. In their tests, they use an example where the area is square. The coverage and communication radii cover 30 points. The HECN is fixed in the centre of the area. In the sequel we consider three examples of areas with three sizes: 350 × 350 points, 500 × 500 points, and 700 × 700 points.
6 For self-regarding, money maximizing subjects the subgame perfect equilibrium prediction in this game is straightforward. Reciprocating reduces the receiver's payoff; hence, the receiver will never reciprocate. Anticipating this, senders will not trust initially. On the contrary, the Pareto efficient solution that provides the highest payoffs is that player 1 trusts and player 2 reciprocates. In our experiment, subjects were randomly assigned to the sender's or the receiver's role. We implemented the game in a simultaneous rather than in a sequential form, as follows: senders had to indicate whether they trusted the receiver or not, while at the same time receivers had to indicate whether they wanted to reciprocate in case the sender had decided to trust them. This strategy method ensured that we collected data on the reciprocity of all subjects who were assigned to the role of the receiver.
Before proceeding to the three games, all participants had to complete the priming task by writing the corresponding piece of text. Three experimenters were present in each session in order to answer any question in private and to assist participants. Whenever participants had finished filling out their booklet of instructions (including the priming text, the three games, and the questionnaires), they were allowed to leave the session. 15 We executed the payments laboratory, he announced his random number to the administration, who recorded the number and associated it to the corresponding person. Afterwards, the administration provided us with additional data associated to each number. In this way we were only able to match a random number to the administrative data and experimental choices.
The sample In the following analysis we pool the data from the low and the high security prisons, given that no significant differences in decisions were found between the two samples in any of the three experimental games. Our sample is composed of 145 subjects in total, 105 of whom are male inmates and 40 (25 female and 15 male) are subjects from the student group. Among the 105 inmates, 57 participated in the priming treatment and 48 in the neutral treatment. 59 inmates were randomly assigned to the role of player 1 and 46 the role of player 2 in the trust game. between the neutral and priming group, and shows that our sample is quite balanced among the two treatments, albeit with a number of exceptions. In the regression analysis of section 5 we therefore control for a number of potentially relevant variables.
Given that participation in the experiments was voluntary, issues of selection into the sample may potentially play a role. In order to alleviate such concerns and assess the extent to which our sample is representative of the general prison population, we compare certain observable characteristics in our sample and among all prisoners in Greece and in the United States. The first characteristic is the distribution of nationalities, differentiating between Greeks and foreigners. For this analysis we use data from the Greek Ministry of Justice, Transparency and Human Rights 24 and find that in our sample, as well as in the prison population in Greece as a whole, foreigners are a majority (70% and 58%, respectively). The second characteristic is the type of crime committed by inmates, for which we have no suitable data for Greek prisons and therefore use U.
Acknowledgements: We are thankful to  Alexander Cappelen ,  Yan Chen ,  Alain Cohn ,  Christoph Engel ,  Marie-Claire Villeval , participants at IMEBESS 2017,  ESA Asian Pacific Meetings 2017 , the Austrian Experimental Workshop 2017,  Bonn-Innsbruck-Munich  workshop, and seminars in Alicante,  Berlin, Birmingham, Cologne, Exeter, Innsbruck, Marburg, Potsdam, Siena, Swansea and UA Madrid , for valuable comments. Special thanks.
Introduction During the last years, the availability of multiple and heterogeneous data sources has given new perspectives to the schema matching problem which is a fundamental step for data integration. A large number of research works exist in the literature, the main task in these approaches is to identify the correlation between the attributes using dataset values, semantic and syntactic rules to detect the correspondence between attributes during the schema matching process  [1]. Most of the works on schema integration assumed a global (mediated) schema and then tried to find a solution for better matching on mostly a pairwise matching between the source schema and the mediated schema. In this context it is very difficult to define a global schema that matches all the attributes of a given domain  [10].
Other techniques, such as Biperpedia  [9] , use search engine query logs in addition to text to discover attributes. This process involves numerous trained classifiers and corresponding labeled training data. Most of the automatic KB construction systems were focused on retrieving facts and entities from unstructured datasets. To our knowledge, integrating the existing structured sources in the knowledge bases has not been considered in the process of constructing the KB automatically. A large number of publications focused only on schema matching. In this context, schema matching identifies the correspondences between similar elements belonging to different schemas. IntelliLIGHT  [8]  is a system looking in large-scale structured data sets which aims to locate and retrieve needed data in a specific domain.
It proposes a method which ranks the main data tables taking as output the ones having a higher score. PROCLAIM is a very different approach to the problem; instead of ranking the best available schema among different data sources, it provides a unified standard schema from all sources and generates a global schema for a domain automatically. UFO  [11]  is a data structure expressing various representations of the same concept as a data object and is capable of recognizing and mapping such objects in different data sources automatically. The WebTable system  [3]  is a search engine that ranks tables scraped from the web. In this approach, AcsDB is introduced as a database which contains a corpus of statistics on schema elements that is used to compute the probability of an attribute (the number of schemas containing the attribute divided by the total number of schemas) and the probability of an attribute conditioned on another attribute.
Column-based data formats Column-based data formats organize data in a set of tables. Each table contains a set of rows, and each row has a set of columns, each with a name and a value. Rows in a table are not required to have the same attributes. Data access operations are usually over individual rows and show the best performances when retrieve only a subset of the attributes of a table, when data sets are sparse and contain lots of empty values  [12]. Moreover column-based data formats process big datasets efficiently since provide largescale parallelization and effective partitioning strategies. PROCLAIM for its calculation needs a tuple for each value of attributes showing the name of the attribute and its value. In this case, storing the data in columnar-based format is much efficient.
Data Type Identification When the search space is large (the number of attributes or schemas are big), matching the complete input of schemas may require long execution times, and achieving high quality results may be difficult. One way to reduce the search space is to find the similar attributes within the same data types. The heterogeneous sources provide attributes in different data types. Since the type of the attributes may not be provided in the metadata of sources, we need to identify the types given the values. One main problem in this step is the fact that the original datasets are not clean. We have to consider the type based on the data type of the majority of the instances (values.
Description Property The majority of datasets have a descriptive part for the schema where the meaning of each attribute can be found. In other cases, the description is not provided but the used values belong to domain specific terms or abbreviations and this description can be retrieved, for example, using domain specific Wikis. To create the description profile, first of all, we remove the stop-words and then we apply the stemming method over a bag of tokens. Then for each description, the stems and the occurrence of each term (in all the different descriptions for any specified attribute) is used to build the description profiles. Removing stop-words in a specific domain is necessary, since these words can appear in almost all descriptions and can cause false similarities (e. g. , for the domain of cars, the words such as car, vehicle, automobile and etc, are the domain stop-words).
Dimensions and units are commonly confused, despite the fact that the solution to most problems must include units. The distribution of the same entity in different units can be shifted, but by consideration of the same dimension, the similarity of shifted distribution can be found. Also, attributes with units related to same dimension are related to each other through a conversion factor, such as Kelvin or Celsius which measures the dimension of temperature and they can convert to each other. Given a dataset, the related units can be found thanks to the descriptive part of the schema or taking into account also the instances (near the value or in a separated column). The units and their mapped dimensions of attributes can be extracted and recorded separately. In Table  2  we show dimensions and units characterizing some attributes of our running example.
This set of top most frequent instances can design a pattern for an attribute. Since other components of attribute profiles are encoded using one-hot encoding approach we decided to apply the same method to the statistics profile. First log transform will normalize the distribution with left or right skewness, then the distribution is presented into categorical scale using binning and finally encoded. We obviously lose the numerical nature of the statistics but we can merge easily this vector with the other vectors without a normalization issue. For each attribute of the dataset, we compute the global profile which is made of four properties we described in this section. Each profile is built by considering the type of attribute and the global profile is finally converted into a numerical vector.
Experiment Results In this section, we provide the experimental results on two datasets: one of them is our ongoing cars example and the second is from the oil and gas domain. The code of the experiment is implemented in Python 3. 6. 7. Parquet  [14] , a columnar datastore is used to store original datasets. Parquet is a free and open-source optimized column-oriented data storage developed on the Apache Hadoop ecosystem. To the best of our knowledge, there are no benchmark labeled datasets for comparing our results with another method. Therefore, for the car example, we have collected data from Kaggle challenges. For the Oil and Gas example, we use a large dataset. attributes) and automatically assign label to each attribute. To be able to evaluate PROCLAIM, we manually labeled all the attributes.
Oil_NorthSea dataset The North Sea Oil and Gas (Oil_NorthSea) dataset were gathered from OGA3 (The Oil and Gas Authority Open Data) website which contains 43997 different sources with a total of 5260 attributes. 4713 of them have different names. The description is available for 3481 attributes and unit is provided for 1668 over 4713 attributes. We apply the same approach as described in section 7. The number of different identified types of attributes is: 638 , 631 , 46 , 574 and 2824 attributes. Since the number of attributes is too big to be entirely manually annotated, we asked domain experts to label random set of attributes (20 labels for and attributes and all labels for attributes -the number of attributes are less than 50). We cannot calculate recall and fmeasure here, since the manual labels are just provided for a subset of random labels.
However, precision is calculated for these subsets for different experiments. Experiments are done for different profiles for each group of same data type attributes and the result is shown in Table  7. Cover data ratio measures the percentage of labeled attributes. The Covered_data ratio is showing a high percentage of considered attributes to discover the global schema. As can be seen, the precision of clusters for , , and data type is over 90% which is a promising result. The global schema created from the Oil_NorthSea dataset contains 247 labeled attributes which covers 86% of the 1315 original attributes belong to the , , and data types.
Car_Kaggle Data set Information as the input for PROCLAIM Car_Kaggle The Car_Kaggle dataset was gathered from five different sources (S 1 ,. , S 5 ) about cars from different Kaggle challenges4. The global Car_Kaggle dataset, after merging different sources contain 78 original attributes: 70 of them have different names; 65 out of 70 attributes contain descriptions and just 9 out of 70 attributes have the provided unit. In Table5, we provide the details of each schema. As first step, we run data type identification in order to discover the type of each attribute. Data for this dataset can be split in four different types and, as we can see the data type is not present. Unique attributes are discarded (6 attributes) and we compute the profile for the 64 remaining attributes (25 , 35 and.
We present JedAI, a new open-source toolkit for endto-end Entity Resolution. JedAI is domain-agnostic in the sense that it does not depend on background expert knowledge, applying seamlessly to data of any domain with minimal human intervention. JedAI is also structure-agnostic, as it can process any type of data, ranging from structured (relational) to semi-structured (RDF) and un-structured (free-text) entity descriptions. JedAI consists of two parts: (i) JedAI-core is a library of numerous state-of-the-art methods that can be mixed and matched to form (thousands of) end-to-end workflows, allowing for easily benchmarking their relative performance. (ii) JedAI-gui is a user-friendly desktop application that facilitates the composition of complex workflows via a wizard-like interface. It is suitable for both lay and power users, offering concrete guidelines and automatic configuration, as well as manual configuration options, visual exploration, and detailed statistics for each method's performance.
INTRODUCTION Entity Resolution (ER) aims to detect di↵erent entity profiles that describe the same real-world objects  [4]. It is a core task for data integration, with many applications that range from knowledge bases to question answering  [6]. Yet, the functionality of the available ER systems is significantly restricted by the format of the various data collections. We can actually distinguish the existing systems into those crafted for structured (relational) data that is described by a well-defined schema, and those applying exclusively to semi-structured data that is associated with loose, diverse schemata and resides in XML/RDF repositories or SPARQL endpoints. The latter category encompasses Link Discovery frameworks, which are surveyed in  [20]. LIMES 1 1 http://aksw. org/Projects/LIMES.
html and Silk 2  are the most prominent representatives. However, most of these tools implement only the method(s) introduced by their creators, and/or are suitable for power users, requiring the manual configuration of matching rules, or a labeled dataset for learning such rules in a supervised way  [14]. Another drawback is that none of them is applicable to structured data, while half of them lack a GUI  [20]. A larger variety of tools is available for structured data. A thorough list of 15 commercial and 18 noncommercial systems (such as Febrl  3  and Dedoop 4  ) is analyzed in  [15]. Most of them, though, su↵er from one or more of the following problems: they cover the ER pipeline partially, they constitute standalone systems with a limited variety of methods, or they are exclusively meant for power users, providing insu cient guidelines on how to perform ER efficiently and e↵ectively  [15].
Magellan  [16]  resolves these issues, o↵ering various blocking and matching methods. However, it is restricted to Record Linkage over relational data, lacks a GUI (it merely o↵ers a command-line interface) and requires heavy user involvement; its goal is actually to facilitate the development of tailor-made methods for the data at hand. Similarly, heavy user involvement is required by the crowd-sourcing systems Corleone  [11]  and Falcon  [5] , which also address ER in an end-to-end manner through various e ciency techniques. To overcome these drawbacks, we developed the Java gEneric DAta Integration 5  toolkit (JedAI for short), aiming to facilitate researchers, practitioners and lay users in applying ER solutions to any type of data. At its core lies the end-to-end ER workflow of Figure  1 , which covers both Dirty ER (Deduplication) and Clean-Clean ER (Record Linkage).
JedAI conveys one of the largest libraries with state-of-the-art ER methods, while being one of the few ER systems that is suitable even for lay users, providing an intuitive GUI. In more detail, JedAI has the following advantages: 1) Structure-agnostic functionality. JedAI applies uniformly to structured, semi-structured and unstructured (free-text) data. 2) Domain-agnostic functionality. All methods implemented by JedAI apply to data from any domain, ranging from homogeneous census, customer, product and bibliographic data to heterogeneous Knowledge Bases. The only requirement is that their entities contain string-dominated values. 3) High time e ciency. JedAI o↵ers the largest variety of blocking and block processing methods. No other toolkit exploits the benefits of schemaagnostic blocking, which minimizes user involvement, while maximizing recall  [22].
Also, no other toolkit includes the Block and Comparison Cleaning steps, which are indispensable for enhancing the time efficiency of ER by orders of magnitude  [26]. JedAI also uses GNU Trove 6 for minimizing its memory footprint. This is done by operating on primitive data types instead of objects. E. g. , collections of integer values are handled through the 4-byte int type instead of the 16-byte Integer objects, thus occupying up to 75% less memory. This also reduces the running time by more than 50% when compared to native Java  [28]. 4) Hands-o↵ functionality. JedAI couples every implemented method with a default configuration of its internal parameters. Thus, neither manual parameter fine-tuning nor expert knowledge are required for building an end-to-end ER workflow; users simply select one or more methods per step.
5) Learning-free functionality. None of the implemented methods requires a labelled dataset for its training. Their default configuration renders them directly applicable to any data. Labelled data in the form of all true matches in a dataset (i. e. , positive instances) are only required for evaluating the performance of a method or workflow and for fine-tuning its configuration parameters. In contrast, the learning-based methods require a labelled dataset for their operation, i. e. , in order to learn their blocking or matching model. This labelled dataset includes not only the positive instances considered by JedAI, but also a carefully selected sample of non-matches (i. e. , negative instances). The Most importantly, JedAI's learning-free methods optimize their performance by fine-tuning their internal parameters, which are generic in the sense that they are independent of the data at hand.