dataesr/scientific-paragraphs-categorization huggingface dataset:
The rates range from 6. 2% out of 741,610 clone pairs for NiCad to 33% out of 103,204 clone pairs for CloneDR. No evidence is provided that the resulting merged clones are acceptable to developers of the affected software projects. Another closely related clone management approach is Cedar  (Tairas and Gray, 2012) , which targets Java and relies on Eclipse refactorings for abstraction. Unlike our approach, Cedar is limited to Type-2 clones. As  Roy et al. (2013)  note, Type-3 clones are particularly common and frequently evolve out of Type-1 and 2 clones. Another work that does clone refactoring was proposed by Zibran et al. (Zibran and Roy, 2013). Although they propose clone refactoring like ours, their approach does not present an algorithm for merging generic near-clones but instead proposes approaches that are combinations of existing software refactorings.
*/ i = strlen ( buffer ); while ( i > 0 && isspace ( buffer [ i -1])) buffer [ --i ] = 0; i f (! strcmp ( buffer , " capabilities " )) { printf ( " * connect \ n \ n " ); fflush ( stdout ); } else i f (! strncmp ( buffer , " connect " , 8)) { printf ( " \ n " ); fflush ( stdout ); switch ( functionId ) { case 1: i f ( b i d i r e c t i o n a l _ t r a n s f e r _ l o o p ( input_fd , output_fd )) die ( " Copying data between file descriptors failed " ); return; break; case 2: return run_child ( child , buffer + 8); break; } } else { switch ( functionId ) { case 1: die ( " Bad command : % s " , buffer ); break; case 2: fprintf ( stderr , " Bad command " ); return 1; break; int _obstack_ b e g i n _ 1 } ( struct obstack *h , int size , int alignment , } void *(* chunkfun ) (void * , long) , } void (* freefun ) (void * , void *) , void *arg) } { s t a t i c void command_loop ( int input_fd , int output_fd ) { /* Common Lines */ c om ma n d_ l o o p _ m e r g e ( intput_fd , output_fd , null , " Input error " , 1); } h -> alignment _mask = alignment -1; h->extra arg = arg; s t a t i c int command_loop (const char * child ) { h -> use_extra_arg = 1; return c o m m a n d _ l o o p _ m e r g e (0 , 0 , child , " Command input error " , 2); } 18 https://github.
RSAT (Regulatory Sequence Analysis Tools ) is a suite of modular tools for the detection and the analysis of cis-regulatory elements in genome sequences. Its main applications are (i) motif discovery, including from genome-wide datasets like ChIP-seq/ATACseq, (ii) motif scanning, (iii) motif analysis (quality assessment, comparisons and clustering), (iv) analysis of regulatory variations, (v) comparative genomics. Six public servers jointly support 10 000 genomes from all kingdoms. Six novel or refactored programs have been added since the 2015 NAR Web Software Issue, including updated programs to analyse regulatory variants (retrieve-variation-seq, variationscan, convert-variations), along with tools to extract sequences from a list of coordinates (retrieve-seqbed), to select motifs from motif collections (retrieve- matrix), and to extract orthologs based on Ensembl Compara (get-orthologs-compara).
INTRODUCTION Initiated in 1998  (1, 2) , the Regulatory Sequences Analysis Tools (RSAT) project aims at deploying software tools to detect cis-regulatory elements in genomic sequences, via a Web interface. RSAT functionalities include de novo motif discovery, analyses of motif quality, motif comparisons and clustering, motif scanning to predict transcription factor (TF) binding sites (TFBSs), detection and analysis of regulatory variants, and comparative genomics to discover motifs based on cross-species conservation (Figure  1 ). Over the last 20 years, the RSAT team has maintained uninterrupted service, while extending developments prompted by the advances in the field of regulatory genomics (Supplementary Figure  S1 ). This Anniversary article gives a 20-year perspective on the software suite, describes its main functionalities, focusing on the novelties since the previous NAR Web server issues  (3) (4) (5) (6) , and presents the various access and training modalities.
RSAT in the high-throughput sequencing era The microarray technology that enabled measuring full transcriptomes and its ChIP-on-chip application opened the field of regulatory genomics to genome-wide analyses. In 2007, the ChIP-seq approach  (13, 14) , taking advantage of high-throughput sequencing technology, revolutionised the field with an unprecedented level of precision and quantity of experimentally-detected functional TF binding regions. In contrast with alternative tools, RSAT programs coped well with datasets obtained from this technology, prompting the development of the user-friendly integrated pipeline peak-motifs  (4, 15, 16) , which enabled the online analysis of full datasets without size restriction. To better cope with the increasing size of datasets, multiple RSAT tools have been optimized over the years to reduce execution time.
Obtaining sequences and homologous genes RSAT maintains locally-installed genomes and integrates on-the-fly access to external databases (Ensembl and UCSC). It offers tools to retrieve sequences relative to annotated genomic features (retrieve-seq for promoter sequences of local genomes, retrieve-ensembl-seq  (17)  for Ensembl vertebrate species). For genome-wide epigenomic datasets where genomic coordinates are usually specified in BED format, corresponding sequences can be extracted from UCSC (fetch-sequences from UCSC) and from local genomes with a new program supporting repeat-masking (retrieve-seqbed). In addition, retrieve-ensembl-seq supports the retrieval of sequences from homologous genes. A new tool also relies on Ensembl Compara  (21)  to return detailed information on homologous genes in a set of reference organisms (getorthologs-compara, currently only for Plants).
Obtaining motifs (PSSMs) The ChIP-seq 'revolution' gave rise to a dramatic increase in the number of PSSMs stored in established motif databases, such as JASPAR  (22) , and a multiplication of independent motif collections. To facilitate the access to motifs, RSAT now locally hosts 50 external motif databases (JAS-PAR  (22) , Cis-Bp  (23) , FootprintDB  (24) , etc. ) (Supplementary Table  S1 ), covering DNA and RNA binding motifs in a wide range of organisms (Metazoa, Prokaryotes, Fungi, Plants). These collections have been homogenised in TRANSFAC format to alleviate format conversion. A new tool enables the extraction of particular motifs from these collections, based on identifiers or names (retrievematrix). The selection menu for motif collections is organised by color-coded taxons and is searchable to simplify access.
Detecting regulatory variations Population genomics and Genome-Wide Association Studies (GWAS) projects produce information on genetic variants (SNPs, indels), many of which are located in noncoding regions of the genome, and may thus affect cisregulatory elements. To predict the impact of sequence variations on TF binding, variants and their flanking sequences can be extracted (retrieve-variation-seq) and scanned with a collection of motifs (variation-scan). This tool has been refactored to support multiallelic variants and indels, and optimized for time efficiency. A new tool further eases file format conversion between VCF, GVF and varBed (convert-variations). Altogether, RSAT sequence variation analysis tools (variation-info, retrieve-variation-seq, convertvariations and variation-scan) enable users to input any motif collection, and either retrieve Ensembl annotated variants, using either IDs or genomic coordinates, or input their own variants of interest (manuscript in prep).
ACCESSING AND LEARNING TO USE RSAT In addition to the public Web sites, RSAT can be remotely accessed via SOAP Web services. RSAT can also be used via Unix command-line, after installation of the suite on a local server or on a computer cloud, either from source code or with a Virtual Machine (on any operating system supporting VirtualBox, including Windows) (3). To learn how to use the RSAT suite, extensive documentation material is available (3). The latest protocols  (25, 26)  describe motif discovery in plant genomes, but the approach can be applied to any organisms supported on the other RSAT servers. Although the Web interfaces are being continually updated, most of our previously published protocols  (10, 15, 27)  are still usable to gain experience in understanding the underlying algorithms, choosing the relevant parameters and interpreting the results.
CONCLUSIONS The RSAT suite is unique for its broad range of functionalities and supported organisms from all kingdoms. The main alternative is the MEME suite  (28) , which mainly focuses on motif analyses. Since the beginning of the project, RSAT strives to facilitate inter-connections with complementary programs (including MEME) and motif databases, thanks to a series of utility tools to convert alternative file formats (convert-background-models, convert-features, convertmatrix, etc. ). Celebrating its 20th Anniversary, RSAT is gearing up for more interoperability with REST programmatic standards, better packaging with conda associated with a Docker image, and centralised documentation on GitHub.
ACKNOWLEDGEMENTS We are particularly thankful to the colleagues who help us installing and maintaining RSAT servers:  Victor del Moral Chavez ,  Romualdo Zayas-Lagunas ,  Alfredo José Hernández Alvarez  ( Centro de Ciencias Genomicas, Cuernavaca, Mexico) ,  Laboratorio Nacional de Visualizaci ón Científica Avanzada (Mexico)  specially  Luis Alberto Aguilar Bautista  and  Jair Garcia Sotelo , along with the ABims platform in Roscoff, France. We thank  Najla Ksouri  and  Chesco Montardit  for providing feedback on the installation of Prunus genomes;  Olivier Sand ,  Matthieu Defrance  and  Céline Hernandez  for regularly answering to RSAT-related questions;  Gabriel Moreno-Hagelsieb  for helping with the Prokaryote genomes. We thank  Mauricio Guzman  for designing all logos for RSAT and styling the figures.
Motif oligo-analysis Sequences Over/under-represented Analyses oligonucleotide occurrences in a discovery oligonucleotides + PSSM set of sequences, and detects over/under-represented oligonucleotides, using various background models and scoring statistics. dyad-analysis Sequences Over/under-represented Detects over-represented dyads (spaced dyads + PSSM pairs of oligonucleotides) within a set of sequences. NGS peak-motifs Sequences Discovered motifs + Discovers motifs in ChIP-seq peak ChIP-seq predicted sites sequence sets, and returns detailed information on sequence composition and discovered motifs, with correspondence in databases and predicted binding sites. Pattern crer-scan Transcription Cis-regulatory enriched Given a set of cis-regulatory elements matching factor binding sites regions (CRER) (predicted sites, annotated sites, ChIP-seq peaks), detects regions presenting a significant enrichment in CRERs.
matrix-scan Sequences + Matching positions in input Scans sequences with one or several (-quick) PSSMs sequences PSSMs to identify instances of the corresponding motifs (putative sites). Supports a variety of background models (Bernoulli, Markov chains of any order). Motif quality *retrieve-matrix Motif collection + Motif (PSSM) From a chosen motif collection (supported and motif name/ID external database), extract the PSSMs comparisons specified by the provided name or (Matrix Tools) identifier. matrix-quality Motif (PSSM) + Score distribution statistics + Evaluates the quality of a PSSM by sequence set(s) ROC curves comparing score distributions obtained with this matrix in control sequence sets. compare-matrices Two sets of PSSM Similarity scores + matrix Compares two collections of PSSMs, and alignments returns various similarity statistics + matrix alignments.
Doerr et. al  [8]  has argued that ontologies for cultural heritage have a tendency to exhibit a focus on the material and physical aspects of the past. The intention behind the use of ontology-based approaches is to allow the integration, use and re-use of the same set of data from different perspectives, especially when considering the high level of 1 https://en. wikipedia. org/wiki/Ontology 2 https://www. w3. org/TR/webont-req/ Track: PROFILES & Data Search: International Workshop on Profiling and Searching Data on the Web WWW 2018,  April 23-27, 2018 , Lyon, France fragmentation of cultural heritage datasets. Beyond a real scientific interest demonstrated by scientists, the use of ontology-based approaches become a major concern in order to push local institution to use this approach, as the case for MIBACT -the Italian Ministry of cultural heritage and Activities and Tourism  [18].
In the CH community, ontologies are mainly adopted for developing Geographic Information Systems (GIS) allowing to visualize, query, analyze, and comprehend geographic data in order to extract knowledges and assist users in their researches. The cultural heritage data that we are dealing with is collected from photographs taken directly from the CH studied site. These photographs are transformed then into 3D orthophoto through a photogrammetric process. Recent works related to GIS approaches  [24] ,  [21]  and for recent spatial approaches based on CIDOC CRM, we cite  [16] ,  [17] ,  [15]. None of these approaches provide a model that described CH artifacts and in the same time the corresponding phtogrammetric process performed in the 3D transformation. Hence, in this paper we present a modeling approach that allows to profile the studied artifacts in term of their typology, spatial information and 3D transformation.
Following linked data best practices  [5] , we linked our ontology to the CIDOC-CRM, which provides an upper level of conceptualization for our model. A simple mapping would not be sufficient, hence, we extended CIDOC-CRM with hierarchical relationships. This alignment is an extension of a previous study  [10]  where we proposed a model for the Montreal Castle in Shawbak, Jordan. Finally, we introduce a new vision of cultural heritage profiling to fill the bridge between ontology modeling and cultural heritage survey where we distinguish between three different dimensions of profiles, typological, photogrammetrical process and spatial information. The rest of the paper is organized as follow. First, Section. 2 will describe the process that we adopted for data gathering from photogrammetry to pattern recognition.
DATA GATHERING AND PHOTOGRAMMETRIC PROCESS: XLENDI WRECK This section describes our process of data gathering that was involved to characterize the Phoenician shipwreck of Xlendi (Malta). We recall here briefly the outline of the process, which lies at a depth of 110m, using modern photogrammetric approaches. The wreck was discovered in 2008, thanks to a systematic sonar survey of the coasts around Malta and Gozo. This study was supervised by the Superintendence of Cultural Heritage in order to uncover all the underwater archaeological remains lying in the Malta's territorial waters. The process applied for the photogrammetric study was done in collaboration with COMEX 3 , a society mainly devolved to underwater exploration missions. They designed a submarine of 2 people, the Remora 2000, on which the acquisition system was integrated.
3D-2D Transformation After the visual odometry phase, which insures a perfect coverage of the site and produces a set of oriented photographs with a correct scale we need to exploit these data in order to produce documents for archaeological study. These documents can be 3D or 2D according to the archaeological needs. The scale is done using stereo calibration of the visual odometry system, a scale-bar constraint is inserted for each stereo pair coming from the visual odometry system. We developed a set of tools to bridge our visual odometry software to commercial software as Photoscan from Agisoft 4  in order to use the densification capabilities. After this first step we obtain a dense cloud of points and a set of oriented high resolution photographs describing accurately the entire site.
ONTOLOGY CONCEPTUALIZATION FOR CH PROFILING Data profiling has a wide definition in different communities, i. e. the definition in wikipedia  5  of data profiling is the process of examining the data available in an existing data source (e. g. a database, photo of object, etc ) and collecting statistics and information about that data. In archeology dictionary wordsmith 6  , the term profile refers to vertical wall, section, or face of an excavation pit that exposes the lateral relationships, archaeological features, structures, stratigraphy -and their relationships. By extension, a profile is a record or graphic representation of these, including color, soil type, and content. Soil profiles consist of a number of layers, or horizons, which result from soil-forming processes. The profiling is the use of profile gauge, i.
e. , a tool for recording the cross-sectional shape of a surface. On the other hand, in semantic web community, a dataset profile can be seen as the formal representation of a set of features that describe a dataset and allow the comparison of different datasets with regard to their characteristics  [3]. To meet the semantic web profile definition with the archaeological one, we provide the following profile definition: Definition. (CH Resource Profile). Let R be a cultural heritage resource and F be a all comprehensive features that describes R. S is a profile for R where S ⊂ F if S is a set of commonly investigated features that describes R with respect to a given application scenario. This set of features allows to profile the CH resource in different application scenarios, i. e. to identify the typology of the resource, to produce a dimensional analysis, to perform a typology clustering and other statistical computations with regard to the represented profile dimension.
XLENDI AMPHORAE SAMPLE DATASET For better understanding of our profiling approach, we made available a dataset on the datahub -XlendiAmphorae 10 , which contains a sample of instances describing Amphore_A15 and Amphore_A03 The typological profile is presented through different morphological attributes that, when collected together, can specify the corresponding typology, i. e. Ramon-T2111-69 for Amphore_A15 and Pithecusse_343 for Amphore_A03. In order to investigate the spatial profiles, we go through the hasTransformation3D relation that point to the instance of the Transformation3D, i. e. Transfo-1953670366 for Amphore_A15. This latter instance provides connections to instances corresponding to the RotationMatrix and the IPoint3D, i. e. respectively Mat1220813917 and IPoint3D1039759545 that together provide information about the spatial location and rotation of Amphore_A15.
Regarding the photogrammetry profile, we draw the reader intention to the RDF file "PhotographSample" in Xlen-diAmphorae dataset which depicts an example of a photograph instance Photograph_13 which is connected to a camera and a 3D transformation. The camera instance is described by a set of camera properties and enriched by a distortion specifications via the relation hasDistortion. The spatial profile is related to a single 3D point called POS (position and orientation system), the photogrammetrical profile describes the photograph with a set of 3D points and a set of rotation matrix that are related trough a single 3D transformation instance.
DATA LINKING During the last years, the increasing adoption of Linked Open Data principles by Web practitioners around the world has led to a growing interconnected web-scale data network, LOD cloud 11. Behind this growth, there is a huge effort of data providers not only to publish their data but also to model and describe them following the LOD best practices. However, to ensure the interoperability of this large scale web of data, we would like to point out to the recommendation of building on, instead of replicating, existing ontologies. Following these best practices, We linked our ontology to the CIDOC-CRM ontology in order to provide more integrity between cultural heritage datasets and to allow more flexibility for federated queries cross different datasets using these ontologies. The current version of this extension relates only to the TBox part of the ontologies by the use of the properties rdfs:subClassOf and rdfs:subPropertyOf to extend CIDOC-CRM schema.
This ontology 11 http://lod-cloud. net/ 12 http://www. opengeospatial. org/standards/geosparql does not provide any specific 3D spatial description of objects, neither geometry bounding boxes. We opted for the skos:closeMatch 13  to describe the link of our ontology to GeoSPARQL, i. e. we linked our ontology to the concepts SpatialObject and Point. In addition to datasets interchangeability, a further motivation behind the connection to GeoSPARQL is to allow an enrichment of the spatial profile in our ontology by the spatial relations in GeoSPARQL, i. e. sfCrosses, ehMeet. To the best of our knowledge and according to the LOV 14 repository, there is no other ontology that provides a model describing the photogrammetry process. Yet, only a few ontologies provide a description for photographic process from which we cite: SIO (semantic science integrated ontology) via the concept "Photograph" 15  and DBpedia that provides a description of the concepts 16.
¥ On the other hand, since we provide the only available data that represents amphorae in the Xlendi wreck with semantic web formats, there is no other available dataset describing this wreck and may contain similar resources to be linked to. Hence, in order to join the LOD cloud 17 , we looked into multi-domain datasets such as DBpedia, where we looked to the widely used concept Camera 18. Let us take the example of the Nikon D100 camera that was used in our photogrammetric process, as depicted in the file "Photograph-Sample" in XlendiAmphorae dataset, and the same camera reference in DBpedia Nikon_D100 19. In our dataset, the calibration, as described in the instance "Distortion1642610353", is the distinguishing criterion between different instances referring to a camera Nikon D100. However, in DBpedia there is only one D100 instance and no calibration differentiation.
Hence, we realized that it is not possible to perform the identity link "owl:sameAs" in this case, according to  [14]. Since the identity link is not able to be adopted, properties such as rdfs:seeAlso 20 or skos:broadMatch might be semantically more appropriate since they indicate a broader matching links. 13 http://www. w3. org/2008/05/skos 14 Searching the term "photogrammetry" in LOV: http://lov. okfn. org/dataset/lov/terms? q=photogrammetry 15 http://semanticscience. org/resource/SIO_000082. rdf 16  As an application of our model in underwater CH scenario as Xlendi shipwreck, archaeologists draw a particular intention to the spatial data mining over amphorae which can be particularly helpful to understand the context of the site, i. e. amphora geo-spatial distribution based on their typologies can help to have an idea of the shape of the ship before it flows, the nature and the origin of the cargo.
Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. Confidence in the results is limited because, (i) different methods give different results, (ii) parameter setting directly influences the final result, and (iii) systematic evaluation of the results is not always performed. Here, we introduce MULAN (MULtiple method ANalysis), which assumes an ensemble based approach combining multiple analysis methods and fuzzy logic to extract graphs with the most probable structure. In order to reduce the dependency on parameter settings, we determine the best set of parameters using a genetic algorithm on simulated datasets, whose temporal structure is similar to the experimental one. After a validation step, the selected set of parameters is used to analyze experimental data.