dataesr/scientific-paragraphs-categorization huggingface dataset:
We mention, for example,  (Hashemi et al. , 2003)  that introduced an entity recognition technique for extracting names, titles and their associations. The problem with these approaches is their non-generality since they are dependent on the language and the domain of text. In addition, the task of defining contextual rules is complex in time and resources. • For data-intensive approaches, entity recognition techniques do not require any explicit grammatical knowledge of the document but obtain their information from an annotated corpus (for example a knowledge database). These approaches treat the problem as a classification task and apply machine learning models to solve it. Different supervised learning methods were used for entity recognition, such as  (Zhang et al. , 2010)  that uses Support Vector Machine (SVMs) for recognizing investigator names in articles.
• Mixed entity recognition methods try to combine the advantages of the machine learning and the rule based approaches. For example,  (Laishram and Kaur, 2013)  combines Conditional Random Field (CRF) with rule definition that helps to identify features for some particular entities to improve the classification by the CRF. In the context of data-intensive approach, the corpus could be a structured database that represents the entities. For matching entities in documents with their representations in a database,  (Chakaravarthy et al. , 2006)  proposed an algorithm, called EROCS, that identifies entities embedded in document segments. It uses a score, defined for an entity with respect to a segment, that considers the frequency of the common terms in the segment and their importance in the database.
PROPOSED APPROACH Figure  1  presents the global schema of the proposed approach. It is a succession of two main modules. The entity resolution module takes as input a database, proposes to link its contained records and constructs an entity model for records referring the same real word entity. It then uses this model to produce as output a cleaned database composed of entities by merging linked records. The entity recognition module proposes to label data in the document. These labels are used to filter segments in the document and entities in the database. Then, this module tries to find the entities in the document segments.
Record Linkage Let E be a structured database that contains n records {r i } and m columns {c j }. Each record r i is composed of m attributes corresponding to each column. The attribute of r i that corresponds to the column c j is noted r i. c j. We propose to group the records in E into q blocks {B k } using regrouping keys. Each block contains records that may refer the same entity. The number of records in B k is defined as |B k |. Let dist(a, b) be the similarity distance between two field values a and b. These latter are decided to be similar if their distance exceeds a threshold T 1. For the linkage decision, the statistical model given by  (Fellegi and Sunter, 1969)  is used. It estimates matching M(p) and unmatching probabilities U(p) for each field of index p. We propose to compute a ratio (ratio) between each pair of records and decide to link them if the value of ratio exceeds the threshold T 2 (see Algorithm 1).
Segment and Entity Filtering Comparing all terms in each segment with all terms of each entity is a costly task. We propose, then, to localize segments that may refer an entity in the database and limit the search to only these segments. This consists of filtering segments in the document focusing only on segments that contain some labeled data. Furthermore, the labeled data is used to filter entities in the database and keep only the records that have at least one field value that corresponds to one label value of the same type. This step of filtering will improve the efficiency of the matching.
Entity-document Matching Model For entity matching in the document with the database, we are inspired by EROCS algorithm  (Chakaravarthy et al. , 2006)  which is described in the following. It perceives the document d as a set of segments d = {s i } where each segment s i is a collection of terms s i = {t j }. Each record of the structured database E is considered as an entity e, having its own terms e = {t l } contained in its attributes. If e is an entity that matches the segment s l , then each term t p in s l corresponds to some term t q of the entity e. The weight of a term t is defined as: w(t) = log((N/n(t)) if n(t) > 0 0 otherwise ( 1 ) where N is the total number of distinct entities in the database, and n(t) is the number of distinct entities that contain t. Let T (e, s) be the set of terms that appear in the segment s and contained as well in the entity e.
EXPERIMENTS For the experiments, we use a database containing a table of suppliers composed of 229345 records. It carries information about suppliers such as their names, addresses and phone numbers. The supplier is the entity of interest. We consider a dataset of 500 image documents and their OCR results. These documents represent industrial invoices or purchase orders. For evaluation, we use a ground truth data containing a table that relates each document with its contained entity identifiers in the database. A document can be The matching algorithm. related to one supplier's identifier or more (in the case of an entity that can reference a client in the document and is present in the DB as a supplier of an other document). This table was manually prepared by an industrial expert.
ICPRAM2015-InternationalConferenceonPatternRecognitionApplicationsandMethods A relevant entity for a document is defined as an entity present in the document and that refers a record in the database. Precision and Recall are defined as: Recall = # relevant matched entities # relevant entities Precision = # relevant matched entities # matched entities Figure  6  shows the evolution of Precision, Recall and F-measure of entity matching in documents with varying the threshold T defined in Section 3. 2. 3. It shows that setting the threshold value at 17 maximize the value of F-measure. This value is retained to evaluate the matching results in the rest of the study. Entity recognition process is evaluated for the main proposed improvement on the original version of EROCS. M EROCS 1 is defined as the first modified version with OCR error tolerance that consists of integrating edit distance in the matching score as shown in eq (3).
In addition, entity resolution solves some ambiguity cases due to the reduction of several records, referring the same entity and maximizing the score, into only one record. It also reduces the algorithm complexity and the execution time thanks to the reduction of the number of compared entities in database for each segment in the document. The error cases of matching are explained. In about 5% of cases, failure is caused by the fact that entity is not well represented in the document. For example, the supplier of document could be represented only by a logo. In about 5% of cases, failure is caused by the bad quality of scanned documents that produces grave OCR errors. In about 4% of cases, it is caused by non-standardized representations of entity terms in the document and the database. In about 7% of cases, it is caused by incomplete entities in database where some fields are missing even after entity resolution step.
CONCLUSION AND FUTURE WORK We present a method, called M EROCS, for entity matching in the database with their representations Recall (%) Precision (%) Fmeasure (%) Runtime (sec/doc) EROCS  (Chakaravarthy et al. , 2006)   The future work is to solve the problem of noncontiguity of elements composing an entity. In case of incomplete entity, we will choose from distant labeled elements those they complete correctly the entity. The choice will be focused on the elements increasing the matching score. Furthermore, we will plan the use of other datasets, limited in this study to supplier entities, in order to enlarge the field search to all elements (close and distant) with more complex spatial relations. The idea is to integrate the physical and logical structures of the document and to exploit them in the element searching.
by segments in OCRed document. The extensions on term matching and segment restructure of EROCS are proven effective for OCRed documents which have altered content and structure. A filtering step based on data labeling reduces the runtime from 70. 8 sec to 6. 2 sec per document. The pre-processing step of entity resolution on the database improves the matching rates with 2. 31 points for the recall and 14. 81 points for the precision and it decreases the runtime with about 1. 8 seconds by document. The results on a dataset of 500 documents are promising and achieve about 73% for recall and about 70% for precision.
by segments in OCRed document. The extensions on term matching and segment restructure of EROCS are proven effective for OCRed documents which have altered content and structure. A filtering step based on data labeling reduces the runtime from 70. 8 sec to 6. 2 sec per document. The pre-processing step of entity resolution on the database improves the matching rates with 2. 31 points for the recall and 14. 81 points for the precision and it decreases the runtime with about 1. 8 seconds by document. The results on a dataset of 500 documents are promising and achieve about 73% for recall and about 70% for precision. 67. 58 54, 09 60, 09 69. 5 M EROCS 1 (+OCR error tolerance) 71, 05 53, 89 61, 29 70. 8 M EROCS 2 (+filtering) 71, 05 54, 77 61, 86 6. 2 M EROCS 3 (+entity resolution) 73,36 69,58 71,43 4,4.
Data The Montreal Neurological Institute research ethics committee gave approval for this study and all subjects gave informed consent. To evaluate the proposed algorithm, two neuroimaging datasets were used anonymously: • From a multi-site clinical study with 67 relapsing-remitting MS patients (RRMS, mean age 37. 5 y, SD 10. 0 y). Each patient underwent an MRI at two time points, baseline (m00 Although the application of our inpainting method is not limited to a specific imaging modality, T1W images were chosen since they are acquired as part of many standard imaging protocols and are widely used to assess longitudinal volume changes in MRI. In addition, this modality was used by the other inpainting methods we wish to compare to in this analysis.
, 1994)  to the stereotaxic ICBM152 template. 2. Tissue classification of H and M: after an automatic segmentation of the WM, GM, cerebrospinal fluid (CSF) and T2W MS lesions (only on the patients) by a multi-spectral Bayesian classifier  (Francis, 2004)  using the T1W, T2W, and PD images. From prior probability model of the segmentation estimated from a training dataset, the M is segmented using Bayes' theorem, where the distribution of each tissue classes is used to estimate the parameters of their Gaussian distribution. The automatic T2W lesion outlines of M were superimposed on T1W, T2W, and PD for manual reviews. Experts who underwent extensive training on similar MS patient MRI data carefully reviewed the MS lesion mask, L. 3. For each H:M pair: Compute the voxel-wise intensity ratio (R) of the healthy WM (obtained from stage 2) intensity average (WMa) and the T1W voxel intensity of lesion tissue [T1W M (i)] from the corresponding manually-corrected mask (L) of M for a voxel i: R M (i) = T1W M (i) WMa 1 ∀i ∈ L ∀i / ∈ L (5) 4.
Estimate the non-linear transformation (NL reg ) between M and H  (Avants et al. , 2008). 5. Using the transformation (NL reg ), interpolate spatially R and L into the H space and obtain R' and L'. 6. From the interpolated R' and L', create a new image (H') where the final image intensity voxels equal R' × H where the lesion (L') is defined and H everywhere else. The six steps are repeated for the 20 H:M pairs. The resulting simulated dataset allowed us to assess the impact of the patch search radius for the proposed NLM inpainting before comparing it to state of the art inpainting approaches. As a means to evaluate the inpainting algorithms, we can assess the fidelity of the restored image I * by comparing it to the original image I. In the computer vision literature  (VQEG, 2003; Wong and Orchard, 2008; Fadili et al.
Longitudinal MS Data Validation In the second set of experiments, our proposed inpainting technique is evaluated and compared to three different publicly available inpainting algorithms and a masking technique using real longitudinal MS patient data in order to determine the impact of the method on the power to detect longitudinal volume changes. The longitudinal MRIs were pre-processed using steps (1) and (2) of Section Longitudinal MS Data Validation in order to obtain the lesion segmentations for each subject's time-point. All inpainting techniques used to the same set of lesion labels for the comparison. In order to compare the performance of the inpainting methods, the popular longitudinal atrophy measurement tool SIENA  (Smith et al. , 2002)  was used to measure the percent brain volume change (PBVC) as well as the percent ventricular volume change (PVVC) in the MS dataset.
Lesion Inpainting on an Individual MS Longitudinal Dataset In Figure  7 , we show examples of lesion filling on longitudinal data from an MS patient using LWM, FSL, LEAP, and NLM. As can be seen on the original T1W images, the lesion boundary has changed between the two time-points. This is likely affecting the inpainting results, since as discussed previously and described in Figure  5 , the performances of LWM, FSL and LEAP are more affected by lesion boundaries than the NLM method. This limitation can be appreciated by comparing the right and left panels of Figure  7 , where the extent of the inpainting "bleeding" into ventricles is different for the different time-points. Clearly, this would lead to erroneous longitudinal measures of ventricular enlargement, for example. In contrast, NLM lesion filling seems to provide more plausible contrast and tissue boundaries gradient that are consistent between both time-points (panel m00 and m12).
The figure shows unexpected focal boundary fluctuations (red arrows) in locations where lesions were present on the "original" image without lesion inpainting and with lesion masking but also with WML, FSL, and LEAP lesion inpainting. These changes are particularly visible in regions of larger lesions (e. g. , peri-ventricular region). The inpainting approaches reduce these fluctuations but the NLM inpainting results show the most homogenous changes across the boundaries. This likely contributes to the  The smallest sample sizes are in bold font. Note, that PVVC cannot be estimated with the Masked-out approach. lower variability that this method provides across the whole dataset.
) and 12 month (m12), that included sagittal T1W (TE = 9 -11 ms, TR = 30 -40 ms, flip angle = 30 • , in-plane resolution = 0. 977 × 0. 977 mm 2 , slice thickness = 1. 5 mm), T2W (TE = 65 -104 ms, TR = 3666 -8585 ms, flip angle = 90 • , in-plane resolution = 0. 977 × 0. 977 mm 2 , slice thickness = 3 mm), and PD (TE = 10 -18 ms, TR = 2200 -3800 ms, flip angle = 90 • , in-plane resolution = 0. 977 × 0. 977 mm 2 , slice thickness = 3 mm) images. The MRI data were acquired on 1. 5T scanners from different manufacturers: GE (n = 20), Philips (n = 18), and Siemens (n = 29). • From this RRMS database, we randomly selected T1W images of 20 MS patients to simulate realistic MS lesions on BrainWeb simulation MRIs (http://brainweb. bic. mni. mcgill. ca/brainweb/; Collins et al. , 1998) from 20 healthy subjects (Aubert-Broche et al.
ACKNOWLEDGMENTS We would like to acknowledge  NeuroRx Research  for providing the expert manual segmentations,  Canadian Institutes of Health Research  ( MOP-111169 & 84360 ), les  Fonds de Recherche Santé Québec  and the  MS Society of Canada  (BioMed PhD studentship, 691 and an Operating grant to DC). This study has been carried out with financial support from the  French State , managed by the  French National Research Agency (ANR)  in the frame of the  Investments for the future Programme IdEx Bordeaux  ( ANR-10-IDEX-03-02 ),  Cluster of excellence CPU  and TRAIL ( HR-DTI ANR-10-LABX-57 ).
In each trial, 6 cycles of one accentuation pattern, or Context, were played. In the 5th or 6th cycle, one of the cymbal sounds in position 1-5 was replaced by the pure tone. Procedure. Participants were given the following instructions (in French): "You are going to hear some rhythms. Your task is to listen to these rhythms and push the space bar as fast as possible when you hear the beep. " The experiment was controlled by a program written in Python and using the library pygame (www. pygame. org). The stimuli were generated on-line, played to the participant and the response time, measured from the onset of the target tone, was recorded at each trial. The computer waited for the completion of the stimuli and for the subject's response before proceeding to the next trial after a 1 second delay.
Acknowledgments Many thanks to  Isabelle Brunet ,  Anne-Caroline Fievet  and  Paul Marty  for their assistance with technical aspects and data collection, and to  Philippe Schlenker  for crucial suggestions from the start. We are also very grateful to  Peter Pfordresher ,  Bruno Repp , and  Mari Riess Jones  for detailed critiques of earlier drafts of this paper. The research leading to these results received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement No. 324115-FRONTSEM (PI: Schlenker) and was also supported by a  Euryi grant  from the  European Science Foundation  (' Presupposition: A Formal Pragmatic Approach '). Research was conducted at  Institut d'Etudes Cognitives (ENS) , which is supported by grants  ANR-10-IDEX-0001-02 PSL Ã  and  ANR-10-LABX-0087 IEC.
The research leading to these results received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement No. 324115-FRONTSEM (PI: Schlenker) and was also supported by a  Euryi grant  from the  European Science Foundation  (' Presupposition: A Formal Pragmatic Approach '). Research was conducted at  Institut d'Etudes Cognitives (ENS) , which is supported by grants  ANR-10-IDEX-0001-02 PSL Ã  and  ANR-10-LABX-0087 IEC.
Finally, the last subkernel compares the depth attributes using the same principle: k d (g ij , h kl ) = e -d G i -d H k 2 /2σ 2 d • e -d G j -d H l 2 /2σ 2 d (3) The full kernel is defined as the combination of the three sub-kernels applied on all pairs of nodes of G and H: K(G, H) = M G i,j=1 M H k,l=1 k a (g ij , h kl )•k x (g ij , h kl )•k d (g ij , h kl ) (4) Note that the number of nodes M G and M H in G and H can be different. Estimating hyper-parameters. The value of the two bandwidth parameters σ x and σ d need to be chosen. We adapt a standard heuristic (used for vector inputs to a gaussian kernel) that selects the median euclidean distance between all observations in the training dataset. We here use for σ x and σ d the median euclidean distance between the coordinates and depth attributes of the nodes in all graphs available at training time.
Mapping gender differences The Open Access Series of Imaging Studies (OASIS, www. oasis-brains. org) cross-sectional database offers a collection of 416 subjects aged from 18 to 96. For each subject, three to four individual T1-weighted MP-RAGE scans were obtained on a 1. 5T Vision system (Siemens, Erlangen, Germany) with the following protocol: in-plane resolution = 256x256 (1 mm x 1 mm), slice thickness = 1. 25 mm, TR = 9. 7 ms, TE = 4 ms, flip angle = 10, TI = 20 ms, TD = 200 ms. Images were co-registered and averaged to create a single image with a high contrast-to-noise ratio. From this database, we selected two groups of 67 male and 67 female healthy right-handed subjects, aged 18 to 34, matched in age. Using this data, our goal was to map gender differences from local patterns of sulcal pits.
In particular, local differences in grey matter volume are to some extent predictive of the differences shown by our results, because they embed information about both cortical surface area, which is linked to folding, and cortical thickness, which can induce local depth changes. Therefore, our searchlight scheme is able to detect local anatomical differences between genders that are consistent with the standard morphometry literature. However, another study that examined group differences between sulcal pit graphs did not find any differences between genders (auxiliary result in  [1] ). We list hereafter four potential reasons that could explain this disagreement: i) our dataset was much larger (134 vs. 26 subjects); ii) our pit detection algorithm is slightly different  [6] ; iii) our kernel provides a graph similarity measure that might be sensitive to distinct properties of the graphs; iv) their study focused on brain lobes, whereas we consider smaller neighborhoods; it might be that the discriminant information lies at a finer scale than brain lobes.
The difficulty is to propose a compositional adaptation which aims to integrate new features that were not foreseen in the design, remove or exchange entities that are no longer available in a given context. We have being focused on the WComp environment  (Hourdin et al. , 2013)  which is a prototyping and dynamic execution environment for Ambient Intelligence applications created by the Rainbow research team of the I3S laboratory, hosted by Uni-versity of Nice -Sophia Antipolis and CNRS. It uses lightweight components to manage dynamic orchestrations of Web service for device, like UPnP (Universal Plug and Play), discovered in the software infrastructure. In the framework of the WComp, it has been defined a management mechanism allowing extensible interference between devices. In order to deal with the asynchronous nature of the real world, WComp has defined an execution machine for complex connections.
The interest of such an approach is twofold: (i) the behavior involved the DEVS models are used to write the methods required in order to code the components when using the WComp environment (automatic generation of WComp code); (ii) the DEVS implementation of the different strategies are used in order to check them before implementation. The rest of the paper is as follows: Section 2 gives the context of the study by presenting the interest in combining DEVSimpy simulation and Wcomp framework for IoT system design. Section 3 presents the background of the work including the DEVS formalism, the DEVSimPy framework and the WComp. Section 4 deals with the validation of the approach through a complex case study involving different kinds of sensors which have been used in order to manage the lighting of a house.
DEVSimPy  (Capocchi et al. , 2011)  is an open Source project (under GPL V. 3 license) supported by the SPE (Science pour l'Environnement) team of the UMR CNRS 6134 Lab. of the university of Corsica "Pasquale Paoli". This aim is to provide a GUI for the M&S of PyDEVS and PyPDEVS  (Li et al. , 2011)  models. PyDEVS is an Application Programming Interface (API) allowing the implementation of the DEVS formalism in Python language  (Perez et al. , ). PyPDEVS is the parallel version of PyDEVS based on Parallel DEVS formalism  (Chow and Zeigler, 1994)  which is an extension of DEVS formalism. The DE-VSimPy environment has been developed in Python with the wxPython  (Rappin and Dunn, 2006)  graphical library without strong dependencies other than the Scipy  (Jones et al. , 2001)  and the Numpy  (Oliphant, 2007)  scientific python libraries.