dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We are grateful to the anonymous referee, whose thoughtful and insightful comments improved the article. We would like to thank the staff of the DPCC, especially  Eric Poujoulet , for their continuous support throughout the years. The authors are grateful for previous contributions by  Tuomo Pieniluoma ,  Hanna Pentikäinen , and  Dagmara Oszkiewicz. The work of GF has been supported by the  Nordic Optical Telescope Scientific Association  and the  Emil Aaltonen foundation. MG is partly funded by the  Academy of Finland  (grant # 299543 ). The work of AD'O and the Italian participation in DPAC has been supported by  Agenzia Spaziale Italiana (ASI)  through grants  I/037/08/0, I/058/10/0 ,  2014-025-R. 0 , 2014-025-R. 1. 2015 ,and  2014-049-R. 0/1/2  to  INAF  and  contracts I/008/10/0  and  2013/030/I.
). In fact, creating together textual descriptions and models by following methodologies (e. g. ODD, UML,. ) improves modeling process and exchanges between scientific disciplines. It opens new perspectives such as dissemination, comprehension, assessment, replication, comparison, theory building, and code generation  (Müller et al. ). However, major modeling projects focus on the model design from the theory of the literature. Data are not taken into account in the model design, and they are separately integrated in an ad-hoc manner  (Truong et al. ; Wang ; Holm et al. ;  Groeneveld et al. ). That is why developed models are o en very theoretical and tend to be far away from the reality (Bykovsky ; Filatova ). Using data to design and parameterize models is still a methodological challenge (Geller ) for which protocols such as ODD seem to respond to.
Shortcomings of existing descriptions. In this subsection, we will show the most critical shortcomings of the current descriptions, and how describing data and data-model connections can improve the empirical foundations of agent-based models. For example, Klabunde et al. ( ) and Filatova ( ) promote an agent-based decision model of migration and an empirical agent-based land market, respectively. These two models (as major ODD+D descriptions) use their own manner to describe data. It shows an inconsistency in the description, and a lack of a unified and comprehensible approach. They do not provide su icient information about empirical data, and how it is structured and used to design and develop the model. The connection between data and model entities is quite confusing: (i) agents' state variables are hard to locate in data; (ii) the relationships between the data structure and the model architecture are omitted.
Note that this paper focuses on the ODD+ D extension, so details on DAMap method  (Laatabi et al. )  are not given here. Nevertheless, DAMap diagram is provided because it is included as a part of the ODD+ D description. The ODD+ D protocol extends ODD+D and allows specifying the usage of data inside a model. It gives new ways to understand and consider data, for a better integration into agent models. By using this protocol, we ought to favor model reuse for another case study. Data-model specification determines using application domains of a model and data that could be applied. It also helps to feed a model with new data of a new case study. ODD based protocols are also tools for modelers to check if all necessary information is available for the model understanding and replicating  (Groeneveld et al.
). ODD+ D adds this aspect for data description. Such protocol can also be assimilated to a media that synthesizes available data in an understandable form. Such description is vital in modeling process to collect data and to make an e icient contextualized analysis of them to extract information and feed the modeling process. This add-on was imagined in order to: (i) be synthetic and precise; (ii) keep sources and understand the done usage of data; (iii) give enough information to use the model with another data; (iv) inform readers about the validity of domain model; (v) facilitate exchanges between disciplines. As such, ODD+ D combines both textual and graphical descriptions, to be understood by a large community.
Questions to answer: Where does data come from? How is it collected? What is the level of available data? How is it structured? How are data tables built from the survey? These are the central questions that users should answer to qualify the data they introduce in the model. Format: Plain text. Contents: Used databases are titled and associated with a short description. Authors must not forget to explain their role in the modeling project and which parts are used. Giving a complete overview of data is beneficial to keep in mind available data along the modeling process  (Hassan et al. )  and to know briefly which kind of data are required by the model. Finally, authors should not forget to associate a hypertext link with each database to allow readers to look for more information.
Data structure. Questions to answer: What are the variables, entities and classes available in data? What do they represent? What is their format? What are their properties? How are they linked?. Format: Plain text, tabular, diagrams. Contents: This block describes the structure of the dataset, and specifies the di erent classes that can become agents in the model. Users are free to explain data structure with plain text, tabular or diagrams, but the clarity and the accuracy of the description is the key to the understanding. Thus, we advise to describe each database with a formalized and unified language such as basic tabular or UML Class diagram; one diagram per database is required. Additional plain text can be fair to give more information about schema for a better understanding and to assume an expertise about data.
Data patterns. Questions to answer: What are the relationships and patterns that exist in data? Are they translated into actions and behaviors in the model? And how do some attribute variations a ect other variables and then agent behaviors?. Format: Plain text, formal language, algorithm. Contents: This block gives a list of patterns and formalizes relations between the database and the agents. It is an excellent description of transformation rules that convert data to agent characteristics. As a result, modelers are advised to specify with accuracy rules by writing equations, formal predicates or algorithms. Plain text documentation could accompany these specifications for a better understanding. Users do not concede the accuracy of this specification of these patterns because the transparency, the readability and the understanding of data analysis are depending on it.
To conduct this study, we collected various types of data: (i) a survey on residential mobility that we performed; (ii) reports produced by the administration of the municipality; (iii) exchanges with local administrations, especially the housing observatory. Data were compiled in few spreadsheets and GIS with their own structure. For example, the dataset storing the survey is organized into categories regrouping attributes about the household structure, the actual dwelling and the next preference that the household wishes to have. Most of the values are coded in numerical form as depicted in Figure , and a codebook is joined with data to explain the meaning of each value (the corresponding file data. xlsx is provided in appendix A). We now present the model we are developing, following the structure of the ODD+D protocol.
ODD+ D Improvements to Describe a Model. The improvements of ODD+ D focus on data description which takes place inside the block "Input Data" of ODD+D. ODD+ D distinguishes four sub-blocks called Data Overview, Data Structure, Data Mapping and Data Patterns. The block "Data Overview" incorporates the contents of the old "Input Data" of ODD and ODD+D protocols, while the three other rubrics introduce new details that seem to be important to understand and replicate the agent-based model successfully. The use of these extended rubrics may help bridge the gap between data and model and push forward solving related problems as introduced in Section : "Shortcomings of the ODD protocol for describing data linking".
Input data Data overview. Data are synthesized based on a survey that was elaborated to collect information about residential mobility and households in Marrakesh, their housing choice and preferences. The database is alimented with broad statistics of Marrakesh extracted from the General Census of Population and Housing (GCPH ). The map we are going to simulate comes from a GIS shapefile of the city we obtained from an online database (openstreetmap. org/node/ , January ). The original dataset is composed of variables we extracted from questions of the survey, which are grouped into four categories: household attributes, dwelling, choice and preference attributes. • Housing choice -expresses the dependence between the chosen destination and the principal variables of the two entities: household and dwelling.
Data patterns • Distance transformation: the distance between a household and a dwelling is a function of four variables ( ). Discussion and benefits of newly added parts. ODD+ D is a guideline that organizes information and ideas according to a determined schedule. This architecture prevents repetitions and favors understanding. Nevertheless, modelers are free in their choices to describe a complex system. Classical ODD descriptions do not give enough information about data use and imputation in agent-based models. ODD+ D gives a response to this lake by (i) keeping the role of Input data block of ODD+D thanks to "Data Overview"; and (ii) expanding it by new blocks ("Data Structure", "Data Mapping" and "Data patterns"). These newly added parts of (Input Data) are necessary to understand the relationship between data and model as we explained before.
Conclusions and Perspectives. In this paper, we argue that the use of data should be more detailed to ease the understanding, developing, validating, replicating and disseminating. ODD+ D description through DAMap diagram prompts the user to choose data, to analyze them, and to link them with an agent-based model. Establishing these direct links synthesizes the whole experience of participants in the modeling process. DAMap diagram is based on graphical languages inspired by UML. A Graphical User Interface permits to draw such visual model and to generate GAML implementation model and ODD+ D textual model. This interface becomes the shared space to discuss knowledge and data to consider. This work shows the e iciency of the approach to support multidisciplinary exchanges and integrate data into ABMs.
Plain text, DAMap diagram. Contents: This block describes the links between data and the agent-based model. Users are welcome to create a DAMap diagram (e. g. Figure ) that gives a synthetic and accurate model of these links. The diagram shows the overall mapping between database schema model (le ) and agent-based model (right), and the processing required to transform empirical data into agent characteristics. It shows, for example, how few columns of database tables are aggregated to determine one characteristic of an agent thanks to a mapping pattern of type aggregation. DAMap proposes two categories of patterns, mapping patterns, and assumption patterns.
Acknowledgment This work is completed in collaboration with the housing observatory of  Marrakesh  (Observatoire régional de l'habitat). We acknowledge stakeholders for their data, their cooperation and their availability. We thank the editor and two anonymous reviewers for their recommendations and constructive comments, which helped us to improve the manuscript. JASSS, ( ) , http://jasss. soc. surrey. ac. uk/ / /. htmlDoi:. /jasss.
Format: Plain text, DAMap diagram. Contents: This block describes the links between data and the agent-based model. Users are welcome to create a DAMap diagram (e. g. Figure ) that gives a synthetic and accurate model of these links. The diagram shows the overall mapping between database schema model (le ) and agent-based model (right), and the processing required to transform empirical data into agent characteristics. It shows, for example, how few columns of database tables are aggregated to determine one characteristic of an agent thanks to a mapping pattern of type aggregation. DAMap proposes two categories of patterns, mapping patterns, and assumption patterns.
The invasive nature of smartphone apps, harvesting personal data has been demonstrated in many studies. The Wall Street Journal reported a study in which 101 popular smartphone apps were examined for personal information gathering activities. Their results showed that more than half of the apps exhibited at least one risky behaviour, such as location tracking, transmission of a smartphone's unique device ID number, or the gathering of other personal information  [14]. A report by Appthority showed that 95% of the top 200 free apps and 80% of the top paid apps for Apple and Android phones did the same  [15]. Chia et al. [16]  studied risk signaling concerning the privacy intrusiveness of Android apps in two repositories. Their results showed that the number of dangerous permissions an app requested was positively correlated with its popularity.
This brought them to the point of allowing users to see the most common misexpectations about an app by revising users' mental model. For this reason, they suggested the use of both crowdsourcing users' mental models and profiling mobile apps using log analysis tools. Amini et al. [20]  employed crowdsourcing as part of a procedure to analyse mobile apps privacy expectation. Participants were asked to rate their expectation and comfort feeling according to the access of sensitive information related to the identified tasks. Thus, by considering the context of apps as well as privacy invasive behaviour, an assessment of the desirability of this information leakage can be depicted. Continuing this work, Amini et al. [21]  envision the tool AppScanner, consisting of different sub-modules, to be able to evaluate mobile apps privacy on a large scale.
Enter Survey In this phase, the participants were given certain questions about their: (1) privacy concern and (2) expectation of the smartphone apps behaviour. We tried to reuse the questions from  [10, 18, 31, 32]  and adapted them to our application domain. The majority of the survey used Likert  [33]  like item scales to measure the privacy concern and expectation of the participants in the area of smartphones. The scores ranged from one extreme attitude (not at all concerned) to another (extremely concerned). In the first set of questions, we collected data on participant demographics, privacy concern and expectation. In the demographics section, we asked participants to provide information on their demographic background, such as their age and gender. We then investigated participants' privacy concern when using a smartphone app in different scenarios such as when the information they shared was considered sensitive in general, or when an app accessed information that did not seem as relevant.
Validation We have implemented PdUC-D in the R programming language  [25] , and we have a wide set of simulations with different configurations. Figure  5  shows a screenshot of the simulation script output, which includes four images: the base pollution map created based on a Kriging-based interpolation, the pollution map created when introducing a random sampling error of 10 ppb (parts-per-billion) for each point, the sampled data (P matrix values), and the areas marked as already monitored (B matrix values). To prepare a suitable data environment, we have created various pollution distribution maps representing ozone levels to be used as inputs. These pollution maps were also generated using the R programming language, following Kriging-based interpolation  [27]. In particular, a Gaussian distribution is used to adjust the parameters coming from random data sources of ozone concentration.
Acknowledgements This work was partially supported by the  "Programa Estatal de Investigación, Desarrollo e Innovación Orientada a Retos de la Sociedad , Proyecto I+D+I  TEC2014-52690-R" , the framework of the  DIVINA Challenge Team , which is funded under the  Labex MS2T program. Labex   MS2T  is supported by the  French Government , through the program "Investments for the future" managed by the  National Agency for Research (Reference :  ANR-11-IDEX-0004-02 ), the  "Programa de becas SENESCYT de la República del Ecuador ", and the  Research Direction of the University of Cuenca.
While Virtual Reality (VR) technologies are commonly used in industrial companies, loading and modifying CAD parts of commercial CAD systems in virtual environments are still challenging. A few VR applications for Computer Aided Design (CAD) enable users to modify native CAD data in an immersive environment. Even if such VR-CAD applications use 3D interaction space, interaction with parameter values of CAD parts could be enhanced. This paper presents ShapeGuide, a technique allowing users to modify native CAD parts using a shape-based 3D interaction technique. With ShapeGuide, users can achieve modification of parameter values by directly pushing or pulling the surface of a CAD object. In addition, force feedback can be integrated into the technique to enhance the precision of the users' hand motions in the 3D space.
In a controlled experiment, we compared ShapeGuide to a standard one-dimensional scroll technique to measure its added value for parametric CAD data modification on a simple industrial product example with an adjusted modification capability. We also evaluated the effect of force feedback assistance on both techniques. Results of this experiment demonstrate that ShapeGuide is significantly faster and more efficient than the scroll technique. Furthermore, they show that the force feedback assistance enhances the precision of both techniques, especially of ShapeGuide.
In this paper, we propose ShapeGuide, a technique to modify native CAD data through a shape-based 3D interaction. ShapeGuide allows users to implicitly manipulate parametric constraints of CAD parts by grabbing and deforming its 3D shape. This technique prepares shape variations from an original design at run-time in order to guide the user hand gesture in 3D space. We compared the efficiency of ShapeGuide on parametric modifications of CAD data with a one-dimensional scroll technique in a CAVE-like system. In addition, we investigated the added value of force feedback assistance on these two interaction techniques. This paper first review previous 3D interaction techniques in VR-CAD context. Sections 3 and 4 detail ShapeGuide and our VR-CAD framework. Then, sections 5 and 6 describe our controlled experiment.
VR-CAD Server In order to deal with native CAD data in an immersive environment, the system needs to interact with a CAD engine in charge of loading original CAD source file and update any parameter modifications into the CAD data structure. To do so, we extended the cRea-VR approach proposed by  Martin et al. (2017)  to make it functional in the distributed architecture, namely VR-CAD Server. Initially, cRea-VR can parse and retain CHG data of native CAD data using CAA V5 (API for CATIA V5 R ) and tessellate its B-Rep elements in an immersive virtual environment. When the system starts, VR-CAD Server loads a specified CAD data (. catpart) and generates related meshes and the linkage information (. xml), which describes the links between each mesh and CHG nodes of the CAD data. This file can be used as a reference to send modification request from VR Platform to VR-CAD Server.
Data Collection We registered 1024 trials: 2 TECHNIQUEs × 2 FEEDBACKs × 4 PARTs × 4 repetitions × 16 participants. For each trial, we logged the time, the evolution of the parameter value and the number of attempts to complete the task. From this data, we extracted four different measures: • Task Completion Time (TCT): the TCT measured the total duration of the modification step during the deformation task. The measure started when the participants selected the part to deform and when all possible shapes were loaded. It stopped when participants validated the deformation with the correct parameter value. If the participants made some errors, the TCT aggregated the time of the different attempts to reach the correct parameter value. The selection step was not considered since it was similar to all the conditions.
20, p < 0. 0001] and  PART [F (3, 225)  = 8. 90, p < 0. 0001], but no significant effect of FEEDBACK, as well as no significant interaction effects between factors. For TECHNIQUE, a Students t-test showed that ShapeGuide (avg. 2. 41s) was significantly faster than Scroll (avg. 4. 14s, p < 0. 0001) (Figure  9 , left 8  ). For PART, a post-hoc Tukey HSD test indicated that the task was significantly longer to achieve for the RightBottomCorner (avg. 3. 64s) than for the RightSide (avg. 2. 94s, p < 0. 0001) and the Bottom (avg. 3. 18s, p = 0. 0017). It was also significantly longer for the LeftBottomCorner (avg. 3. 34s) in comparison to the RightSide (avg. 2. 94s, p = 0. 0112). To check if the consistency between the gesture and the shape deformation had an impact on TCT, we also analyzed the data by grouping parts which produced a consistent and an inconsistent deformation motion according to the gesture direction with the Scroll technique.
Overshoots A similar analysis to the one for WDS rate was conducted for Overshoots. For TECHNIQUE, a Wilcoxon Signed Rank test showed that ShapeGuide led to significantly more Overshoots (avg. 0. 54) than Scroll (avg. 0. 23, p < 0. 0001) (Figure  11 , left). However, for FEEDBACK, a Wilcoxon Signed Rank test revealed that Force significantly reduced the number of Overshoots (avg. 0. 33) in comparison to NoForce (avg. 0. 45, p = 0. 0240)  (Figure 11, right). For PART, a Friedman test was conducted and rendered a Chi-square value of 14. 71 which was significant (p = 0. 0021). A post-hoc analysis detected that the RightSide induced significantly less Overshoots (avg. 0. 26) than the RightBottomCorner (avg. 0. 52, p = 0. 0023) and the LeftBottomCorner (avg. 0. 45, p = 0. 0189). We also ran a REML analysis on Overshoots with the model TECHNIQUE×FEEDBACK×PART×RAND(PARTICIPANT) after that the data was aligned with the ART procedure.
DISCUSSION The results of the experiment provide evidence that ShapeGuide technique significantly increases user performance on parametric modification of CAD data in comparison to a one-dimensional scroll technique used as a baseline. More precisely, participants were able to achieve the deformation task 42% faster with ShapeGuide than with the Scroll technique, which supports H1. This improvement can be explained by a better consistency between shape deformation and user hand motion with ShapeGuide. In particular, we observed that ShapeGuide reduced of 80% the chance that participants move their hands toward the wrong direction at the beginning of their gesture, in comparison to Scroll. This is especially true on parts which produce an inconsistent deformation motion according to the  gesture direction (RightBottomCorner and Bottom): Scroll on the inconsistent parts led to significantly more wrong direction starts than on the other parts.
To implement such function, the scale value should be computed to guarantee that the minimal distance between available shapes fits human hand motion precision capabilities. With respect to this issue about Overshoots, the results show that magnetic force feedback can be a effective solution to reduce FIGURE 14 | Native CAD data modification in a user-oriented environment. A user modifies the shape of Rear-view mirror within a realistic virtual environment of a car's cockpit while perceiving modification impact from a first person perspective in real time. the number of Overshoots and thus, improves the precision of both techniques, and especially ShapeGuide. During the experiment, participants achieved the deformation task with 27% fewer Overshoots with the magnetic force feedback than without, which supports H4.