dataesr/scientific-paragraphs-categorization huggingface dataset:
INEX Social Book Search Track and Test Collection SBS task (http://social-book-search. humanities. uva. nl/) aims to evaluate the value of professional and user's metadata for book search on the Web. The main goal is to exploit search techniques to deal with complex information needs and complex information sources that include user profiles, personal catalogs, and book descriptions. The SBS task builds on a collection of 2. 8 million book description crawled by the University of Duisburg-Essen from Amazon (http://www. amazon. com/) [13]  and enriched with content from LibraryThing (http://www. librarything. com/). Each book is identified by an ISBN and is an XML file. It contains content information like title information, Dewey Decimal Classification (DDC) code (for 61% of the books), category, Amazon product description, and so forth.
Thus, we just need two tags instead of all tags in XML, the ISBN and the whole content (named text). Secondly, Indri (http://www. lemurproject. org/indri/), Lemur Toolkit for Language Modeling and Information Retrieval was used to carry out a language model (SDM) described in Section 4. 2. Indri is a framework that provides state-of-the-art text search methods and a rich structured query language for big collections (up to 50 million documents). It is a part of the Lemur project and developed by researchers from UMass and Carnegie Mellon University. We used Porter stemmer and performed Bayesian smoothing with Dirichlet priors (Dirichlet prior  = 1500). In Section 5. 1, we have described our approach based on DGD which includes graph processing. We used Net-workX (https://networkx. github. io/) tool of Python to perform shortest path computing, neighborhood extraction, and PageRank calculation.
Results. In this section, we discuss the results we achieved by using the InL2 retrieval model, its combination to the SDM model, and retrieval system proposed in our approach that uses the graph structure DGD. We used two topic sets provided by INEX SBS task in 2014 (680 topics) and 2015 (208 topics). The systems retrieve 1000 documents per topic. The experimental results, which describe the performance of the different retrieval systems on INEX document collection, are shown in Table  2. As illustrated in Table  2 , the system that combines probabilistic model InL2 and the language model SDM (InL2 SDM) achieves a significant improvement comparing to InL2 model (baseline). The two systems do not provide similar level of retrieval effectiveness (as proved in  [16] ). The results of run InL2 DDG PR using the 2015 topic set confirm that incorporating PageRank scores using our approach based on DGD network improves ranked retrieval performance but decreases the baseline performances when using the 2014 topic set.
Conclusion and Future Work This paper proposed and evaluated two approaches of document retrieval in the context of book recommendation. We used the test collection of INEX Social Book Search track and the proposed topics in 2014 and 2015. We presented the first approach that combines the outputs of probabilistic model (InL2) and language model (SDM) using a linear interpolation after normalizing scores of each retrieval system. We have shown a significant improvement of baseline results using this combination. This paper also proposed a novel approach based on Directed Graph of Documents (DGD). It exploits social link structure to enrich the returned document list by traditional retrieval model (InL2, in our case). We performed a reranking method using PageRank and ratings of each retrieved document.
). Footprints carry information about the events that have produced them and thus, they can be used by investigators to reconstruct the events which happened during an incident. In a digital context, a footprint may be a piece of information about web browser activity, a document or a file left in the bin. There is a large number of footprints sources (Forensics Wiki, 2007)  (Gudhjonsson, 2010) : • First, web browsing and emails can be used to get information about the user behaviour on the web. Each web browser stores in files or in databases a large number of potentially useful information for investigators. Regarding the web browser Mozilla Firefox for example, it is possible to obtain information about the webpages visited, the content entered into form fields, the bookmarks and downloads performed by a given user.
In the operating system Windows for example, footprints can be collected from several locations: o Windows event log EVT and EVTX record information about various kind of events such as session login, start/stop service or software, error occurred during the execution of a program, installation of a new software, etc. ). o The registry is a database containing a large amount of data stored as keys. It stores information about system configuration, devices or software configuration. o Prefetch and superfetch folder are used to speed up the loading of applications which are used on a regular basis. For each start of software, a file containing information about the software (loaded data, locations used, etc. ) is created in the prefetch folder. These files are a potential source of information for the investigator as each of this file allows to see the name of the executable, the name of files used by this latter, the number of uses of the software and the date of the last launch.
o The recycle bin and the restore points allow to discover deleted files which are potentially interesting for the investigation. • Logs of software are also a rich source of information. For example, antivirus logs contain information about exploits and malicious software detected on the computer. Server logs such as Apache logs or Microsoft IIS logs can be used to get information about query sent to the server. • Content of files can be used for multiple purposes during an investigation. In addition, metadata associated to each file allows to know how and when a file was produced and who created it. For example, image metadata contains information about the user (location), the camera used, the date on which the image was taken ,etc. XMP metadata used for PDF files allows to know the title, the author or the creation date of a document.
Another example is the MAC times used by file systems. These allows to know when a file has been modified, accessed, created or last modified (in Linux). When the extraction of footprints is completed, investigators need to convert them into events and build a timeline containing all the events related to the incident. This timeline allows investigators to have a global overview of the case and to know for example what machines was used, what applications were running or what files have been modified at a given time. An event is a single action occurring at a given time and for a certain duration. An event may be the drafting of a document, the reading of a webpage or a chat conversation with somebody. Each event carries temporal information allowing to know when the event occurred. This information takes the form of a time interval to define the beginning and the end of the event and implicitly, its duration.
• The approach is able to cope with problems related to the preservation of the integrity of data used during the investigation. ECF: Event Correlation for Forensics purposes  (Chen, Clark, De Vel, & Mohay, 2003)  argue that it is possible to correlate the information contained in computers (log files, etc. ) despite the heterogeneous nature of data. The ECF architecture proposed in this work is made of a storage element containing events extracted during an investigation in addition to tools able to settle and query this database. ECF is composed of events parsers, a database and a user interface. An overview of the ECF architecture is given in Figure  1.
ECF proposes parsers to handle sources such as Apache logs, Windows 2000 logs or door logs. • Dynamic queries: this interface allows the investigator to query the database. Queries are built by assembling constraints on one or more fields of the event table using Boolean operators. For example, the investigator may look for events occurring between two dates or search for all events caused by a given person. • Custom queries: this interface allows to execute directly SQL queries. Therefore, this interface provides more flexibility to the user. • Hypotheses testing: this tool allows the user to create new events and test the validity of these assumptions. The main contribution of this work is the introduction of an architecture able to gather events from heterogeneous sources into a single structure.
). • The result of the event (success, failure or unknown). Each event can also carry a number of additional attributes to allow the storage of more specific information. The main purpose of this approach is to provide automatic mechanisms to convert events extracted from heterogeneous sources to high-level events which are easier to understand for an investigator. To reach this objective, several concepts are introduced by the authors: • Raw event: event contained in the event sources such as log files. • Logical event: event stored in canonical form in the database. • Simple event: logical event resulting from the conversion of a raw event. • Composite event: logical event resulting from the aggregation of several logical events. To convert the raw events in logical events and to construct composite events, Event Logical Patterns (LEP) are used.
The proposed ontology also includes temporal information on events in addition to causal relationships between events. The FORE architecture is composed of three parts which are the extraction module, the ontology and the analysis tools. In the extraction module, a set of parsers is used to extract knowledge from various sources such as Apache server logs, Windows 2000 logs, etc. The knowledge extracted is then used to populate the ontology with new instances of events. Each parser is dedicated to a specific type of source allowing to take into account the specificity of each source. Then, automated tools are proposed to process this knowledge. A correlation tool based on rules is used to identify causal relationships between events ("if event A is the cause of event B then event A has to occur to allow event B to occur").
E), the level of details of above ground element representation in DEM (buildings, sidewalks, etc. ) (var. S), and the spatial discretization resolution (grid cell size of a regular mesh) (var. R). Parameter var. E follows a probability density function whereas parameter var. S and var. R are discrete operator choices. The coupling of an environment for parametric computation (Prométhée) and a code relying on 2D shallow water equation (FullSWOF 2D), Promethée-FullSWOF 2D (P-FS) tool has been set up. P-FS tool allows launching directly numerous set of computation using R software. 1200 simulations of a river flood event scenario were performed on the regular computational mesh, spatially discretizing a 17. 5 km 2 urban area (Nice, France). The aim is to produce UA over points of interests and SA through Sobol index maps production.
Indeed, above ground surface features (buildings, walls, sidewalks, etc. ) that influence overland flow path are densely present. These structures have a high level of diversity in urban and industrial areas and create a complex environment for overland flow. Category of numerical codes based on 2D SWEs use with high resolution topographic information provide a valuable approach which has been previously tested for runoff modeling scenario at industrial site scale  [Abily et al. , 2013a , Abily et al. , 2013b]. In the context of fluvial flood events modeling over a large urban environment scale, 2D SWEs based modeling tools are intensively used in practical studies. Urban reconstruction relying on airborne topographic data gathering technologies such as imagery and Light Detection and Ranging (LiDAR) scans are intensively used by Geomatics communities  [Musialski et al.
It gives the possibility to select useful informations for a DEM creation specifically adapted for flood modeling purposes  [Abily et al. , 2013b]. Even-though HR classified data is of high horizontal and vertical accuracy (in a range of a few centimeters), this data set is assorted of errors and uncertainties. Moreover, in order to optimize models creation and numerical computation, hydraulic modelers make choices regarding procedure for this type of dataset use. These sources of uncertainties might produce variability in hydraulic flood models output. Dealing with uncertainties in hydraulic models is a press-forward concern for both practitioners  [Iooss, 2011]  and new guidance  [ASN, 2013]. Sources of uncertainties in hydraulic models come from a hypothesis in mathematical description of the natural phenomena, from input parameters of the model, from numerical aspects when solving the model.
It allows robustness of model results to be checked. Various methods are then available to rank parameters regarding their impact on result variability (such as Sobol index). This process goes one step beyond UA and constitutes a global sensitivity analysis (SA). SA stands in our study for global sensitivity analysis methods (omitting screening and local analysis methods) see  [Jacques, 2011]. In practice, such type of approach is of a great interest, but is still at an exploratory level in applied studies relying on 2D SWE codes. Indeed, SA approach implementation is challenging, as it requires specific tools and deals with important computational capacity. The purpose of the study presented in this paper is to provide both, (i) a tool and a protocol, to allow adaptable and ready-to use SA for 2D hydraulic modeling applications and (ii) to tackle impact of uncertainties related to HR topographic data use, over the variance of h max calculated by 2D SWEs codes.
Two categories of uncertain parameters are considered in our approach: • first category is inherent to HR topographic data internal errors (measurement errors); • second category is due to operator choices for this data inclusion in hydraulic codes. The study is performed over the lower part of the Var river valley using an estimated hydrogram of the 1994 flood event. HR classified topographic data have been made available for the study area, which is 17. 5 km 2 , by Nice municipality. The HR topographic data are included in numerical models under the form of DEM which is directly used as a computational grid (structured mesh). Three uncertain parameters are studied: the measurement error (var. E), the level of detail of aboveground element representation in DEM (buildings, sidewalks, etc.
) (var. S), and the spatial discretization resolution (grid cell size for regular mesh) (var. R). A process using a coupling of an environment for parametric computation (Promethée) and a code relying on 2D shallow water equations (FullSWOF 2D) is developed (P-FS tool) and implemented on a high performance computing structure (Marseille Mésocentre). Aerial Photogrammetry technology allows to measure 3D coordinates of a space and its features using 2D pictures taken from different positions. The overlapping between pictures allows to calculate 3D properties of space and features based on stereoscopy principle. To measure accurately ground and features elevation, a step of aerotriangulation calculation is necessary, requiring information on picture properties regarding their position, orientation and bonding points.
A low flight elevation, a high number of aerial pictures with different points of view and high levels of overlapping, allow to increase the accuracy and the reliability of the 3D coordinates measurement. In photogrammetry, the spatial resolution is the size of a pixel at the ground level. For 3D classified data set creation a photo-interpretation step is necessary. Photo-interpretation allows creation of vectorial information based on photogrammetric dataset  [Egels and Kasser, 2004, Linder, 2006]. A photo-interpreted dataset is composed of classes of points, polylines and polygons digitalized based on photogrammetric data. Important aspects in the photo-interpretation process are classes definition, vectorialisation methods and dataset quality used for photo-interpretation. These aspects will impact the design of the output classified dataset  [Lu and Weng, 2007].
Low Var river Valley HR 3D classified data A HR photogrammetric 3D classified data gathering campaign has been held in 2010-2011 over Nice municipality, covering 400 km 2 [Andres, 2012]. The pixel resolution of aerial pictures is 0. 1 m at the ground level. Features have been photo-interpreted under vectorial form in 50 different classes by human operators. These classes of elements include large above ground features such as building, roads, bridges, sidewalks, etc. Thin above ground features, like concrete walls, road-gutters, stairs, etc. , are included as well in classes. An important number of georeferencing markers are used (about 200). Globally, over the whole spatial extend of the data gathering campaign, the mean accuracy of the classified data is 0. 3 m and 0. 25 m, respectively in horizontal and vertical dimension.
Errors in photo-interpretation are estimated to represent 5% of the total number of elements. To control and ensure both, average level of accuracy and level of errors in photo-interpretation, the municipality has performed a terrestrial control of data accuracy over 10 % of the domain covered by the photogrammetric campaign. For the low Var river valley area, a low flight elevation combined with a high level of overlapping among aerial pictures (80 %), have conduced to a higher accuracy level. In the low Var river valley sector, classified data horizontal and vertical mean accuracy is 0. 2 m. The total number of classified 3D polylines over this area is above 1,200,000. For our application, the 3D classified data of the low Var river valley is used to generate specific DEM adapted to surface hydraulic modeling.
Physically based model -FullSWOF 2D The name FullSWOF stands for Full Shallow Water equations for Overland Flow  [Delestre et al. , ]. It is a set of open source C++ (ANSI) codes, freely available to the community (GPL-compatible license CeCILL-V2) from the website http://www. univ-orleans. fr/mapmo/soft/FullSWOF/. The structure of the code is made to facilitate the development of new evolutions. This software resolves the shallow water equations  [Cunge et al. , 1980]  thanks to a well-balanced finite volume method based on the hydrostatic reconstruction (introduced in [Audusse et  al. , 2004 al. , ,Bouchut, 2004]] ). This numerical method has good properties: water mass conservation, well-balancedness (at least preservation of a lake at rest equilibrium) and positivity water height preservation.
Validations of FullSWOF 2D have already been performed on analytical solutions (SWASHES  [Delestre et al. , 2013] ), on experimental data and on real events at small scales (agricultural parcels  [Delestre et al. , 2014] ). The shallow water system in 2D (SW2D) writes:    ∂ t h + ∂ x (hu) + ∂ y (hv) = 0, ∂ t (hu) + ∂ x (hu 2 + gh 2 /2) + ∂ y (huv) = gh(S 0x -S f x ), ∂ t (hv) + ∂ x (huv) + ∂ y (hv 2 + gh 2 /2) = gh(S 0y -S f y ),. (1) The first partial derivative equation of system (1) is the mass conservation equation and the two other ones are the momentum equation where the 2D vector (u, v) is the fluid's horizontal average velocity across a vertical column of height h(x, y, t) [m] and g = 9. 81 is the acceleration due to the gravity. The subscript x (respectively y) stands for the x-direction (resp.
As previously mentioned, this study focuses on two categories of parameters introducing uncertainty, in output of interest h max calculation. These parameters are related to accuracy errors in HR classified data and to operator choices when building DEM and when integrating this DEM in hydraulic model. One parameter (var. E) encompasses uncertainty related to HR topographic data measurement errors. Two parameters (var. S and var. R), represent uncertainty introduced by operator, respectively when building a specific DEM (var. S) and when spatially discretizing topographic information within 2D hydraulic code (var. R). Var. E, S and R properties are detailed in the next section. A sample of 2,000 DEMs which are used directly as structured mesh in the hydraulic code FullSWOF 2D is created. This sample of DEMs combines all the possibilities of selected sets of the three input parameters var.
S, R and E. Aim is to produce a database of variable output of interest (h max ) combining systematically parameters var. S, R and E. This experience plan might not be optimal, but will allow to proceed to Monte Carlo sampling within the output database, verifying convergence of the Monte Carlo run, through the use of bootstrap test as a post-treatment phase. To run the 2,000 hydraulic simulations, Prométhée software is coupled with FullSWOF 2D. Prométhée is an environment for parametric computation allowing to carry the uncertainties propagation study when coupled to a code. This software is an open source environment developed by IRSN (http://promethee. irsn. org/doku. php). Interest of Prométhée lies in the fact that it will allow to parameterize input of any numerical code and is optimized for intensive computing resources use.
Moreover, statistical post-treatment can be performed using Prométhée as it integrates R statistical environment. The coupled code Prométhée / FullSWOF (P-FS) is used to automatically launch parametrized computation. For UA and SA, the deterministic code FullSWOF 2D is considered as a blackbox model as described in  [Marrel et al. , 2012] : where f is the model function, X = (X 1 ,. , X p ) are p independent input random variables with known distribution and Y is the output random variable. The UA will be done over the selected points of interest (figure  4 ). It is a first approach for analysing impacts of the three parameters over h max. Analysis is planned to be performed comparing the distribution at different points of h max (minimum, maximum, mean, standard deviation and probability distribution shape).
5 m resolution by DIGNCA. The HR DEM resolution is here degraded to 1 m resolution. At this resolution the number of mesh cells is above 17. 8 million. Then, a selection procedure among classified data is performed. This selection is achieved by considering concrete elements which can influence overland flow drainage path only. It includes dikes, buildings, walls and "concrete" above ground elements (such as sidewalks, road gutters, roundabout, doors steps, etc. ). 12 classes are selected among the 50 classes of the 3D photo-interpreted dataset (figure  1 ). During this step, polylines giving information on elevated roads and bridges, which might block overland flow paths, are removed. The remaining total number of polylines is 52,600. Selected above ground features are aggregated in 3 groups of features (buildings, walls and concrete street features).
Results and perspectives Modifications on FullSWOF 2D code allow to run described river flood event scenario. A proof of concept of 3D HR classified data use for river flood modeling is given here (figure  5 ). Advantages of such an approach rely on (i) possibility to include detailed surface elements influencing overland flow, and in automatization and modularity of class selection for HR DSM production and, (ii) taking advantage of FullSWOF 2D numerical properties of mass conservation, well-balancedness and positivity preservation, which are relevant for HR overland flow modeling in urban areas. Performed version of P-FS couple allows to run simulations with a selected set of input parameters (var. E, S and R). Through R commands, it is possible to launch serial calculation. The coupled tool is operational on the HPC Mésocentre.