dataesr/scientific-paragraphs-categorization huggingface dataset:
Drawbacks: the graphical display doesn't show the successive steps experienced by the statistical units (individuals or portions of space) to move from one state to another. For example, we can't know if the growing area of the transportation facilities is provided by the decreasing area of agricultural land use, by the decreasing area of the forest land use, by both or by other growing land uses. Moreover, this display isn't strictly longitudinal: it could be applied to a cross-sectional sample of the general population at each time step. Display 2 -The second example is the sequence index plot (Figure  2 ). It represents a sample of the first ten persons or polygons' trajectories of each dataset, describing each state at each step of each individual. For Ex. 1, each trajectory is composed of one (individual n°1), two  (individuals n°2, n°4, n°5, n°7, n°8, n°9)  or three (individuals n°3, n°6, n°10) successive states.
Ex. 2 -Changes of land use: agricultural lands state in 1982 and built areas state in 2008 are the two main states which concentrate most of the main trajectories beginnings and ends. Furthermore, the most frequent trajectories show land use changes from agricultural lands to built areas, parks and recreational spaces or forests, but it is also difficult to identify in which periods the changes were the most numerous. Drawbacks: the parallel coordinate plot for sequence data is not suitable to represent large samples of longitudinal data, especially when the individual trajectories are complex (several states or events and significant heterogeneity in individual behavior). To conclude this short state of the art, the sequence distribution plot (Figure  1 ) provides a cross-sectional view of the distribution of states at each time step, but it loses the longitudinal aspect of the dataset.
The slide plot: technical implementation The slide plot function is available on the following GitHub repository: https://github. com/ hcommenges/slider. It relies on two R packages: reshape2 (Wickham, 2007) and ggplot2  (Wickham, 2009). The first one is used to reshape the data, the second one implements the grammar of graphics  (Wilkinson, 2005)  used here to design the slide plot. • Choose a group. The value assigned to the factor variable group (e. g. "male" or "female" if the chosen factor is the sex of the individuals) for which you want to explore the data. You can perform interactive selections by choosing up to two factor variables and, for each of these, one or several modalities. The performed selection is an intersection (AND boolean operator): for example, by choosing "female" (sex) and "retired people" (occupational status), the pseudo-syntax query is as follows: WHERE SEX = "FEMALE" AND STATUS = "RETIRED".
The output of this selection is a sub-population of retired women. The main panel is made of seven tabs providing tables and plots to explore longitudinal data. Each of these outputs can be downloaded with the Download button. It renders a SVG vector file with a default size of 20cm (w) x 15 cm (h). You can set the size from 1 to 30 cm. Be aware that index plots can be disk-space consuming when involving a lot of time steps and/ or trajectories. The data summary tab indicates the number of observations, the number of variables and shows a sample of 25 rows of the table. The default number of rows may be changed and the table can be searched, filtered and re-ordered. The transition rate tab shows the frequency of transitions from one state to another, as observed in the dataset. Three options are available: absolute frequencies, row percentage and column percentage.
The slide plot tab draws the graphical display described above. Four options are available: • Threshold. The slide plot might draw a huge number of micro variations which may harm the readability. Values under the set threshold are treated as residuals. The threshold value is considered as the minimal value. • Minimal thickness. The thickness of each segment is proportional to the frequency of the aggregated trajectories it represents. The thickness depends on three parameters: the threshold parameter, the maximal value of the dataset and the minimal thickness parameter. The minimal thickness is assigned to the threshold value and the maximal thickness is assigned to the maximal value. For that reason, you can modify the overall appearance using both parameters: threshold and minimal thickness.
Conclusion 30 The SLIDER application provides a user-friendly interactive platform to explore longitudinal data and to export graphical results in vector files. It offers a new graphical display, the "slide plot", which is useful to explore a wide range of phenomena grasped by longitudinal. It provides a graphical interface to render the slide plot (SLIDER application) and it provides the R code to load the slide plot function in a native R program (SLIDER code). In addition, the SLIDER application grants access, in a user-friendly interface, to four graphical displays implemented in the TraMineR package.
RESEARCH METHOD The research was carried out as a case study, a method of studying contemporary phenomenon in a real life context  (Yin 2009). We collect data by interviewing two projects teams conducted by Information Technology Department (ITD) of a French Public Institute. Less than 1% of staff works in this ITD. The interviews were semi-structured. The questions' themes were: RE concepts, RE process, documentation, and practices. We have noted all answers and summarized them. Finally, we sent them to interviewees for review and correction. We have also collected data by interviewing stakeholders about three domains (biomedical research, financial and human resource) involved in these projects. In summary, the RE's state is drawing in Figure  n°1.
There are many mechanisms that exist for transferring knowledge from one firm to another for example, documents, project reports, face-to-face meetings, telephone calls, e-mails, video conferences or personal transfer. The important aspect for transferring knowledge is to choose a suitable method of knowledge transfer for the different types of knowledge being transferred  (Martin and Antonio 2010). Explicit knowledge can be transferred through, for example books, documents, databases and meetings. On the other hand, tacit knowledge can be transferred through personal interactions, meetings, training and learning by doing. Social interaction ties have positive effects for resource exchanges between organisations  (Mirani 2006; Grim-Yefsah 2012).
The intestinal physiology of Drosophila melanogaster can be monitored in an integrative, non-invasive manner by analysing graphical features of the excreta produced by flies fed on a dye-supplemented diet. This assay has been used by various labs to explore gut function and its regulation. To facilitate its use, we present here a free, stand-alone dedicated software tool for the analysis of fly excreta. The Ultimate Reader of Dung (T. U. R. D. ) is designed to offer a flexible environment for a wide range of experimental designs, with special attention to automation and high-throughput processing. This software detects the distinctive changes in acid-base and water balance previously reported to occur in response to dietary challenges and mating. We have used T. U. R. D. to test the contribution of the bacterial environment of the flies to various intestinal parameters including the established diet-and mating-triggered responses.
Image analysis The Ultimate Reader of Dung is written in C++ using OpenCV, AlgLib  (Bochkanov)  and the Qt Framework (Qt project), and is released with a GNU General Public License. We encourage researchers to take advantage of its open source nature to develop it further and adapt it to their specific needs, under the terms of the GNU GPL. The software can be downloaded from http://sourceforge. net/ projects/the-ultimate-reader-of-dung/, together with a collection of faecal pattern sample images. Refer to http://the-ultimatereader-of-dung. sourceforge. net/# for installation and quick start instructions as well as additional documentation on software design and algorithm implementation. The Ultimate Reader of Dung is currently available for Windows and Mac OSX systems. T. U. R. D. detects faecal spots by applying an adaptive threshold to the greyscale-transformed image.
Analysing faecal spot data with The Ultimate Reader of Dung Flies fed a diet supplemented with a dye will deposit their excreta as small spots that can be detected on a clear plastic surface  (Cognigni et al. , 2011). We sought to replicate and combine the graphical and statistical functions required for the extraction and analysis of faecal spot data without the use of commercial software. To do so, we have taken advantage of open source libraries to construct a stand-alone software tool referred to as The Ultimate Reader of Dung (T. U. R. D). T. U. R. D identifies spot objects from an image obtained from a scanner, extracts the graphical features of each individual spot and produces aggregate descriptions and statistical analysis of samples based on user-defined experimental groups. More details about the algorithms used for each of these steps are available on http://the-ultimate-reader-of-dung.
Working with data sets The faecal spot data extracted from each image file are processed to generate a summary of plate-level features. This associates the filename, ID, timestamp and detection protocol used for each processed image to the information extracted about biologically meaningful variables (namely number of spots, area, IOD, shape and colour parameters), calculated for the entire set of spots and for the ROD and non-ROD subsets separately. The plate-level output of T. U. R. D. can be used to compare faecal patterns across plates and determine the effect of treatments or manipulations. To do so, each plate can be included in user-defined comparison groups. The software can calculate Descriptive Statistics for each group, displaying mean, standard deviation, median, minimum and maximum for each plate-level variable.
This is particularly crucial when trying to show a lack of effect (see below), in which case the correction should not be applied. More advanced statistical treatment, especially important in the case of large datasets and multivariate experimental designs, should be carried out using dedicated statistical software. Each level of analysis in T. U. R. D. (deposit data, plate-level summaries, and descriptive and comparative statistics) can be exported in Comma Separated Value format and easily imported into various data analysis software packages. The exported files can also be used to generate graphical reports when entered into appropriate software. For example, Fig. 2  shows graphs generated in R (R Development Core Team, 2008) with faecal pattern information imported from the T. U. R. D.
High throughput processing T. U. R. D. allows the user to process multiple plate images in batch and automatically generate annotated images and faecal spot reports, while keeping a record of the image files and parameters used for image processing to provide an audit trail. The data are organised by experiment in a relational database in order to deal with their hierarchical nature. The multi-thread architecture allows for parallel processing of images. We hope these advancements over the previous commercial software options will prove useful for higher-throughput designs such as screens. Other steps of the protocol, namely the preparation and scanning of the faecal spot plate, can also be optimised to facilitate high-throughput workflows. The assembly of the Petri dish setup can be simplified with the use of a food cutter (Fig.
3a ) and image acquisition can be standardised with matching scanner guide moulds and digital masks (Fig. 3b  and http://the-ultimate-reader-of-dung. sourceforge. net/#). With the use of macro-enabled image processing software such as ImageJ  (Abramoff et al. , 2004) , these steps can be automated to extract and label multiple data files for each scan, ready to be entered into T. U. R. D. for batch processing. Further optimisations, such as labelling of plates with barcodes or QR codes, are available through open access resources and can be introduced into the image processing workflow.
Acknowledgements We thank  Bernard Charroux  for assistance and  Scott Waddell  for support. Our work was funded by a  Wellcome Trust RCDF  ( WT083559 ), an  ERC   Starting Grant  and  MRC  intramural funding to I. M. -A. , an  ANR  Grant ( ANR 2010 JCJC 130401 ) to F. L. and a  BBSRC studentship  to P. C.
Novel Augmented Reality techniques have the potential to have a large positive impact on the way remote maintenance operations are carried out in hazardous areas, e. g. areas where radiation doses that imply careful planning and optimization of maintenance operations are present. This paper describes an Augmented Reality strategy, system and implementation for aiding the remote collimator exchange in the LHC, currently the world's largest and highest-energy particle accelerator. The proposed system relies on marker detection and multi-modal augmentation in real-time. A database system has been used to ensure flexibility. The system has been tested in a mock-up facility, showing real time performance and great potential for future use in the LHC. The technical-scientific difficulties identified during the development of the system and the proposed solutions described in this paper may help the development of future Augmented Reality systems for remote handling in scientific facilities.
Materials and methods The system proposed in this paper is intended to be used for remote handling and remote maintenance in the CERN environment. The developed AR system for remote collimator exchange relies on marker detection and provides multimodal interaction and augmentation capabilities. Markers have been the solution selected for the recognition process to ensure the performance in real-time. A database system has been used to provide flexibility for the development and maintenance of the final application. The main actor (i. e. the user) is in charge of defining the configuration of the camera to be used for the current session. This configuration is fed to the acquisition module which starts the camera with the provided configuration and begins the acquisition of video frames. The frames are fed to the marker detection module and to the rendering module.
System description The marker detection module receives the frames and starts the recognition and tracking of the markers visible in the image. This module provides two outputs: the unique identifier of the markers and the position and orientation of the markers in the 3D world. This information is fed to the database module and to the rendering module. The information about the content (e. g. virtual elements associated with every marker identifier, the path of the files, and the information to display in the menu) is located in the database module. All this information is provided to the multimodal augmentation module and to the menu module, according to the information received from the marker detection module. The multimodal augmentation module receives the information of the content data and processes the files before the actual rendering.
This pre-rendering step is needed as the virtual elements to be rendered come from very different sources (e. g. videos, images, audio files, etc. ). Therefore, they have to be read by the system and unified for the final rendering. When the content has been unified, it is fed to the rendering module. The multimodal augmentation module is also in charge of receiving the interaction of the user with the system and react accordingly to this input. The system also provides the option of displaying a menu over the final augmented view. The menu module communicates with the database module to obtain the required information to display in the menu, including the information related to the current maintenance step and provides layer to be displayed to the rendering module. Finally, the rendering module receives the inputs of the rest of modules (i.
From all fiducial markers, binary-based markers seem to be the most robust  [32]. The marker detection module proposed in this paper has been designed to detect binary-based markers, using ARToolKit  [33]  as the base library. These markers are represented as 3 Â 3 matrices of black and white squares (see Fig. 3 left ). Each square represents a binary number, creating thus a number that uniquely represents every marker. One advantage of this approach is that the markers require no previous training and that it allows lower processing times as markers do not need to be tested against an increasing number of patterns. Table  1  shows results obtained from Ref. [27] , where processing times for fiducial markers and binary-based markers are presented. Thus, by using binary-based markers the system can be scalable as the processing time does not increase significantly when a larger number of markers have to be used.
Fig. 3  shows an example of a marker (left) and the collimator and collimator crane with the attached markers (right). The two main aspects in the development of an AR application are the marker/markerless positioning system (explained in the previous paragraphs) and the rendering system. The rendering system used in this work uses OpenSceneGraph  [34]  as base library. The multimodal augmentation module is in charge of unifying the Human Computer Interaction (HCI) capabilities of the system. It is in charge of the pre-processing of the virtual content (e. g. 3D models, images, videos, plain text, browsers and voice instructions) and of the user input by means of keyboard, mouse and/or touch screens. There are around 100 collimators along the LHC tunnel (this number could increase up to 152 in future) with up to eight different configurations  [35].
For this reason, the number of different 3D models and other virtual elements may be relatively large. Instead of hardcoding the paths of the virtual elements inside the application as a large number of AR systems do (e. g. [36] ), a database system has been used in order to create and maintain the system without code modifications. The database contains all information required to properly load and display the virtual elements in the augmented scene. Moreover, the steps definition process has also been moved to the database system, so that the different steps can be added, edited or modified without the need of recoding the application. The database system has been designed to contain three levels of the maintenance process: maintenance, job and task or step. Maintenance is the top level and can comprise one or more jobs.
Each job can contain one or more tasks (steps) which are made up of one or more virtual elements (see Fig. 4 ). In the database, every maintenance, job and task has its own name, identifier and description. All the info about the elements to be displayed (file path, size, position with respect to the marker, etc. ) is also defined in the database. The database has been used locally, but it can be seamlessly used as distributed database with distributed virtual elements. As a result, the virtual elements can be stored and managed in a centralized system or split into different computers and accessed from any device connected to the same network. The AR view comprises the video feed from the camera and the virtual elements augmenting it in real time. There are some elements that do not need to be included in the 3D world (i.
3D models Collimators and other scientific equipment are usually designed using 3D software tools before the final construction. However, those 3D models are usually CAD models which file formats are not always compatible with 3D rendering engines. Thus, the need of a format conversion for the 3D models is frequent in order to use them in the AR application. The models used for the collimator case were created using CATIA V5. These models have been exported to STEP format, imported into 3ds MAX and exported to a suitable format (i. e. IVE files) for the rendering engine. The models designed in CATIA are usually highly detailed (e. g. the collimator model utilised for the prototype contains 2,106,058 vertices and 2,524,451 polygons) as they are the base for the construction of the real equipment.
Also, the loading time is reduced using the specific parts. The average loading time (including database access and loading in memory) of all models when using the specific parts is around 1 s while it takes an average of 2 s when using the full simplified models. Although the difference may not seem to be too large, it may be significant when the number of involved 3D models increases. However, in any case, this process is done only once at the beginning of the execution and it does not affect the framerate. The main challenge in the model preparation process is to properly align the models with the markers to provide a coherent visualization in the AR scene. The position of the marker in the real environment has to be known beforehand, in order to properly align the 3D object and its pivot point with respect to the real equipment in order to achieve a realistic augmentation.
Conclusions In this paper, a first prototype for an AR based application used for the aiding of collimator exchange process at LHC has been presented. AR has been previously applied for human intervention maintenance applications in the literature with good results. However, there are only very few examples of AR applications for remote handling maintenance. The proposed prototype uses AR technology to facilitate the collimator exchange from a remote location. A modular system has been developed to acquire the video feed and process it in order to provide a final multimodal augmentation. Due to the special characteristics of the target equipment, several issues, such as camera or marker selection, have been taken into account during the development. A database system has been developed and integrated in order to provide a unified method for creating and maintaining the application.
Another important aspect to develop is the implementation of an authoring tool that enables non-programmers to create and maintain the application. As a result of the use of the database system, the application can be modified without the need for recoding. However, a user-friendly application should be created to allow a faster and easier creation or modification of the application, as new collimator models or different instructions may appear. As it has been mentioned before, the zoom feature is implemented from the main camera. However, in the future it may be suitable to replace the zoom with the video feed from a second camera that provides a new angle or a closer view. As the number of different collimators is already large and can be increased in a future, a sustainable system has to be designed in order to enable the increase of new models.
This component is equipped with two main modules: Key updating and data encryption modules. 1. Data encryption module: When CH confirms the malicious behavior of suspected node or malicious CH is detected by the IDSs (see intrusion detection module), the selected CH (a new CH in case when the older is an attacker) informs all nodes located within the same cluster, i. e. by sending an Encryption_message, to activate data encryption processes with computing new keys (see key updating module). We note that, this module uses a simple and fast cipher algorithm called Skipjack  [14]  to encrypt and decrypt data. As a result, an external passive attacker cannot eavesdrop the packets that pass thought its radio range, which allow data privacy within the network. It is important to note that, choosing Skipjack lies in the fact that it is very simple in both its operation and its key schedule and also it is the best algorithm in terms of memory requirements, with approximately 2,600 bytes of ROM  [15].