dataesr/scientific-paragraphs-categorization huggingface dataset:
However, this learning relying intensively upon the triggering of head movements, it is also necessary for the robot to understand when this knowledge is robust and relevant enough, thus not requiring further motor reaction. The HTM is part of a much wider system, implemented as the TWO!EARS software 1  , which aims at providing a computational framework for modeling active exploratory listening that assigns meaning to auditory scenes. More precisely, it consists in perceiving and analyzing a multimodal world through a paradigm that combines a classical bottom-up signal-driven processing step together with a top-down cognitive feedback. In there, the HTM is in charge of building an internal semantic map of the explored environment, made of localized audiovisual objects coupled with their respective semantic importance, the so-called congruence.
In comparison with other works, the proposed system is described as a real-time  (Huang et al. , 2006)  and online behavioral unit, which is always able to learn new situations while also taking advantage of its previous experience of the past environments. In terms of architecture, the proposed system receives data from several "experts" from the TWO!EARS software, i. e. , computational elements specialized in very particular tasks, such as the identification of sounds or images. It means that the HTM system is placed right after these experts, and thus receives already highly interpreted data. Two main parts constitute the system: an attentional component, the Dynamic Weighting module  (Walther and Cohen-Lhyver, 2014) , and a learning component, the Multimodal Fusion & Inference module  (Cohen-Lhyver et al.
Conclusion The Head Turning Modulation system is composed with two modules: the Dynamic Weighting module (DW) and the Multimodal Fusion and Inference module (MFI), each of them having been described in this section. The DW module is an attentional component, working on the sole basis of observed data in unknown environments, from which it enables the robot to turn its head to audiovisual sources considered as "of importance. " Coupled to it is the MFI module that learns the relationship between the modalities that are used to define an object (audition and vision in this case). Based on a Multimodal Self-Organizing Map (M-SOM), the MFI module is able to create the knowledge required by the DW module to work properly. This knowledge consists in the fusion of multimodal data into a corrected database of audiovisual categories, knowledge that is created through online active self-supervised exploration of the audiovisual sources appearing in the unknown environments.
Experimental Setup and Data Generation The overall system has been evaluated in a realistic environment by using a real robot integrating the whole TWO!EARS software and evolving in a real room. In practice, two different robots have been actually used: one mobile platform from LAAS-CNRS (Toulouse, France) named JIDO, the other one from ISIR (Paris, France) named ODI, see pictures in Figure  9. Both platforms support a KEMAR HATS (Head And Torso Simulator), whose necks have been motorized to control their head movements in azimuth  (Bustamante et al. , 2016). A HATS is a manikin endowed with two microphones placed inside two pinnae which mimics the acoustic effect of the head (and torso) on the left and right ear signals, thus producing a realistic binaural information, close to what a human could actually hears.
Everything related to the platform and data acquisition is handled by the ROS middleware, running directly on the robot: navigation, obstacle avoidance, image and audio captures, etc. Note that a dedicated ROS binaural processing node has been developed during the project, so that most of the audio cues required for sound localization, recognition and separation are directly computed in real-time on the robot. State-of-the-art ROS nodes dedicated to vision (acquisition and processing) have also been used. All the data computed on the robot are then transmitted to another computer running the TWO!EARS framework thanks to a MATLAB-to-ROS bridge. This bridge has been entirely designed to deal with the proposed bottom-up and top-down approach of the project, so that all the ROS nodes can be easily parameterized on the fly and in real time.
Then, all the steps required for the "cognitive" analysis (i. e. , object localization, recognition, fusion, etc. ) runs under MATLAB. Experiments used in this paper have been conducted in a pseudo-anechoic room populated with loudspeakers over which QR codes have been attached to, see Figure  9C. These are used by a ROS node to extract the visual labels of each object directly and with a recognition rate similar to the one obtained through the binocular vision of JIDO with the Line-Mod algorithm  Hinterstoisser et al. (2012). All the sounds emitted from the loudspeakers belong to a database constituted of sounds used to train the audio experts in recognition. In other terms, all the sounds can be recognized by at least one expert in the architecture. Then, the HTM has been evaluated in experimental conditions by two scenarios: the first emphasizes the global behavior of the system, while the second focuses on the fusion and classification abilities of the MFI module.
Note that the audio experts used in the following experiments have been set up by using data from a database recorded in a different acoustic environment. Since they all rely on a prior learning step exploiting these data, there will be a mismatch between their learning and testing phase. The main consequence is mainly a lower frame recognition rate, evaluated to about 37% for the four classifiers used here, and that have been chosen amongst the most performing ones  (Two!Ears, 2016a). The same applies to the localization algorithm, with less consequences: experiments still show a good ability to localize sounds with a precision of about 7. 7 • (including front-back confusion). Finally, the visual recognition of QR codes works almost perfectly, while being quite sensitive to changes in illuminations.
Finally, the coupling of the HTM system with other cognitive experts in the current framework is still under investigation. So far, the current version of the TWO!EARS software does not include others high-level cognitive experts. Nevertheless, the entire HTM system has been conceived with the idea that the motor exploration can also be guided by cognitive elements other than the ones implemented in the system. For instance, a model as the one recently proposed by  Lanillos et al. (2015)  on attention driven by social interaction, could easily be linked to the HTM, both benefiting from each other: one congruent source could still be focused because of its social interest, whereas a socially non-interesting object could still be focused for its high incongruence.
Satellite remote sensing Over the last decade, significant advances in remote sensing techniques have led to a more complete overview of the water cycle at the global scale. Satellite earth observation can provide direct information to processes such as estimation of evapotranspiration  [16] , precipitation  [17] , and snowcover/snowmelt  [18, 19]. Indirect methods, such as the coupling of remote sensing data with groundwater models, can be used for the assessment of the infiltration process and the recharge of the aquifers. Launched in 2002, the NASA Gravity Recovery and Climate Experiment satellites (GRACE) is the first satellite mission able to provide global observations of terrestrial water storage changes. Many negative trends have been observed in north-west India  [20] , the Middle East  [21] , northern China  [22] , the Caspian Sea and the Aral Sea regions  [23] , and the southern part of the La Plata basin  [24].
Other researchers, see  [31]  for example, facilitated the telemetric monitoring technology in order to investigate a natural and isolated lake in Northern Taiwan which was inaccessible for extended periods due to extreme climatic conditions caused by typhoons. Telemetric monitoring systems consist of two principal components: the field equipment and the base station equipment. The field equipment in general includes measuring sensors, a data logger system for data storage and a modem for data transmission, while the base station includes the database and the appropriate software. At the sensor level, the primary issues are the sensor suitability for placement in a field environment, its cost and its power requirements  [31] , with sensors for physical parameters, such as temperature, moisture, light, etc.
The Soil and Water Assessment Tool (SWAT) model, for example, is a robust watershed modeling tool that uses the ArcSWAT interface, which is an extension to ArcGIS environment to create its inputs  [54]. Similarly, a lot of the models developed by the U. S. Army Corps of Engineers Hydrologic Engineering Center (HEC), such as the Ecosystem Functions Model (HEC-EFM), Hydrologic Modeling System (HEC-HMS), and River Analysis System (HEC-RAS), have a set of procedures, tools, and utilities for processing geospatial data in ArcGIS using a graphical user interface (GUI) for the preparation of data. However, this significant advantage of GIS capability to incorporate related spatial data into traditional water resources databases and then retrieved by water-related models can also be exploited by programming and the development of custom graphical user interfaces (GUI).
In other words, GUI is a bridging software that makes the communication links between the GIS databases and the model interface by transforming the data on the format that is required by the model. GUI and query languages permit rapid selection and modification of attribute data and parameter values, allowing for swift sensitivity analyses and multiple scheme evaluation  [51]. Prior to performing actual simulation, water resources modeling requires a number of timeconsuming steps, including collection, compilation, storage, retrieval, and manipulation of spatial data. With their ability to combine various data sets, GIS software changed the way water resources modeling is handled  [55]. In surface water hydrology at river basin scale for example, the data required for the assessment of hydrological parameters such as time of concentration, infiltration rate, etc.
Geo-information cloud databases The development of cooperative databases has been fostered by the improvement of GIS technology which, among its other capabilities, combines the storage of descriptive and observational information with coverage characteristics  [59]. At the same time, with the continuous emergence of new Internet technology, GIS are becoming more open and accessible, thereby facilitating the democratization of sharing spatial data, open accessibility, and effective dissemination of information  [60]. Furthermore, the implementation of a relational database management system (RDBMS), which is related to geographical objects through geodatabases, enables the coupling of spatial information with tabulate data in order to store, update, manage, and properly allocate information.
This means, for example, that a substance of concern that is recorded by a gauge station of a telemetric monitoring network can be connected to a number of spatial elements, such as the downstream water body, inhabited areas, and environmental protected areas. In the latest years, both open source (OS) and commercial geo information systems are being routinely used, not only for sharing spatial datasets and monitoring observations but also for advanced geoprocessing functions across the Web  [61]. In order to bypass interoperability problems and the data not only be easily accessible but also easily operated, international communication standards, including Open Geospatial Consortium (OGC) standard-compliant services, have been developed to support the establishment of Spatial Data Infrastructures (SDI)  [62].
The OGC Web Processing Service (WPS), for example, defines a standard interface to access geoprocessing functions through Web services, while the Catalog Service for the Web (CSW) defines a standard way for publishing and discovering geospatial resources. Similar approaches to the structure of spatial data have been adopted at European level by the INSPIRE Directive of the European Community (EC)  [63]  that triggers the creation of a European spatial data infrastructure which delivers integrated spatial information services linked by common standards and protocols to users. The evolution of the WebGIS systems in the early 1990s from the use of the Extensible Markup Language (XML) to the later use of geography mark-up language (GML) and scalable vector graphics (SVG) as well as the integration of Web Feature Services (WFS) and Web Map Services (WMS) to the current WebGIS systems is presented in the literature  [64, 59].
Currently, one of the most up to date technologies for spatial information sharing is the Google Fusion Tables (GFTs). GFT is a cloud computing database that provides services on the Web for data management and integration. These services can be accessed directly over the Internet through a browser and permits programmatic access via application programming interface and integration of existing tabular data. The specific technology works by exporting data values from the tables created online or from a user provided spreadsheet and converting it into a meaningful graphical data representation. This collaborative scientific platform has started penetrating into the scientific community. In particular, GFTs coupled with Google Earth were used for time-critical geo-visualizations of the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Deepwater Horizon oil spill imaging campaign  [65].
In this case, the GFT service was applied to create a highly interactive image archive and mapping display, while its Application programming interfaces (API) was utilized to create a flexible PHP-based interface for metadata creation as the basis for an interactive data catalog. Researchers combined GFT with the OGC sensor observation service (SOS), which can provide real-time or near-real-time observations, in order to manage and analyze in situ sensor observations of soil moisture due to its impacts on agricultural and hydrological processes  [66]. The literature also shows that GFTs were used for the development of a geo-referenced information system developed for the transboundary aquifers in Africa. The aim of this system was to provide appropriate tools under a Web-based platform for water management institutions in the region  [59].
Following this technological trend, in 2016, IHP and the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California-Irvine launched the iRain mobile application, devoted to facilitate people's involvement in collecting local data for global precipitation monitoring (http://en. unesco. org/news/irain-new-mobile-app-promote-citizen-science-andsupport-water-management). The specific application allows users to visualize real-time global satellite precipitation observations, track extreme precipitation events worldwide, and report local rainfall information using crowd-sourcing functionality to supplement the data. The specific application works together with the PERSIANN-CSS tool and provides real time observation for the amelioration of the remote sensing precipitation estimations.
Monitoring networks for water On the field of monitoring networks, the importance of groundwater resources is denoted by the Global Groundwater Monitoring Network (GGMN). GGMN is a participative, Web-based network of networks that was set up to improve quality and accessibility of groundwater monitoring information and subsequently the knowledge on the state of groundwater resources at global scale. GGMN is a UNESCO IHP programme, implemented by the International Groundwater Resources Assessment Centre (IGRAC) and supported by many global and regional partners. The GGMN portal (https://ggmn. un-igrac. org/) contains information on the availability of groundwater monitoring data through space and time, and through the portal, groundwater level data and changes can be displayed on a regional scale.
By providing updated precipitation observations at the global level, the usefulness of the GeoServer is a direct support to meteorological drought monitoring and early warning worldwide and a contribution to the Global Framework for Climate Services (GFCS) hosted by WMO  [71]. Moreover, the UNESCO Chair International Network of Water-Environment Centres for the Balkans (INWEB) has developed, with the use of different technologies, cloud-based databases for the internationally shared aquifers in the SE Europe, North Africa, and in the Middle East. These databases provide dynamic maps where descriptive information and aquifers characteristics can be easily retrieved by the users with the use of the customized Graphical Users Interface (GUI). The utilization of the Google Fusion Tables Technology by the UNESCO Chair INWEB resulted in the construction of a prototype geo-referenced information system developed for the transboundary aquifers in Africa  [59].
The knowledge gained by the aforementioned geo-information system was used in the UNESCO-IHP's activities on coastal groundwater-related wetlands in the Mediterranean region, under the MedPartnership project (http://www. inweb. gr/fusion/coastal_wetlands/ coastal_wetlands. html), Figure  2. In particular, after the population of a Web database with the 82 coastal aquifers and the 26 wetlands characteristics, JavaScript and HTML5 Web programming languages were used for the creation of the platform interface and the linkage with the database. The final output aimed at (i) facilitating water users to easily retrieve data (spatial and descriptive) related to the coastal wetlands in the Mediterranean, (ii) informing users for the relation of coastal wetlands with underlayed coastal aquifers, (iii) supporting public participation by allowing users to provide comments (either general comments or comments related to a specific geolocation) on the water bodies and environmental areas that appear on the base map, and (iv) enhancing communication by automatically generate emails to specific workgroups whenever comments are made.
As a result of the GEF-funded Transboundary Waters Assessment Programme (TWAP) project, UNESCO-IHP and IGRAC executed an assessment of 199 transboundary aquifers and 43 Small Island Developing States (SIDS) groundwater systems. The data include indicators describing the hydrogeological, environmental, socioeconomic and governance dimensions of tranboundary aquifers and SIDS groundwater systems and are available on IGRAC's Global Groundwater Information System (GGIS) (https://www. un-igrac. org/global-groundwater-information-system-ggis). UNESCO-IHP is highly involved in the promotion of ICTs tool for water resources management. In June 2013, the UNESCO-IHP launched the Hydro free and/or Open-source software Platform for Experts initiative (also known as HOPE: http://www. hope-initiative. net/).
The initiative brings together experts from several fields of water resources to engage in capacity building and trainings based on the use of Free and Open Source Software (FOSS)  [72]. Indeed, the FOSS provides reliable sustainable basis for scientific decision-making, which is essential for the sound governance of water resources. In that sense, HOPE offers a more integrative, international and solutions-oriented approach, with the aim of linking high-quality focused scientific research to policy-relevant interdisciplinary efforts for global sustainability. Because of its decreased software costs, FOSS contributes to improve access to technologies and more specifically in the developing world. The initiative also intends to stimulate cooperation in research and development and to enhance their dissemination.
Users can combine those layers of information to create maps tailored to their own needs. Transparency and respect of authorship are guaranteed as all information provided benefit from metadata in a standardized format and from a Digital Object Identifier (DOI). This allows for an accurate identification and crediting of any contribution and easy later sharing. Inter-disciplinary collaboration, professional networking, and mentoring are also stimulated through working groups, where users can exchange and provide feedbacks on their ongoing work. This involvement and participation contributes to the building of an online community. By gathering global and inclusive knowledge on water, and facilitating interdisciplinary collaboration, IHP-WINS aims overall at supporting Members States and stakeholders involved in resources management.
Conclusions The way in which nowadays information is shared and communication takes place has changed the perspective of our world. The recent revolutionary progress of science, technology, and global communication has put new standards at national and international level, while this progress is also transforming the structure of the economic and social activities. The present work demonstrates that the exponential advances of the information-communication technologies (ICTs) have been also encapsulated in the environmental sector, with special emphasis to be given to the water sector, and international organizations, such as UNESCO-IHP, have adopted this potentiality in their various programs. The specific work gives also emphasis to the use of ICTs that have been used in different UNESCO IHP programs in order to (i) strengthen the capacity building of water management institutions to implement sustainable forms of utilization, management and protection of transboundary water resources, (ii) facilitate water users to retrieve data related to transboundary water resources, (iii) enable water experts to share data, and (iv) support public participation.
[74]framework of the promotion of open-source and free software, UNESCO is coordinating with the Vrije Universiteit Brussel, the OpenWater Symposium. The symposium focuses on sharing experiences newly lead research using open source software and open access tools within the field of water management and hydrology. UNESCO-IHP is also behind the Water Information Network System (IHP-WINS), which was launched in January 2017[74]. This online platform (available at: http://ihp-wins. unesco. org/) incorporates GIS data on water resources into a cooperative and open-access participatory database to foster knowledge-sharing and access to information. IHP-WINS is freely made available by UNESCO's IHP to Member States, water stakeholders, and partners with the aim of facilitating access to information and encouraging contributors to share data on water.
Previous Works The methods mentioned in Section 1 are used in some existing systems to obtain dynamic shape measurements. For instance, Lenar et al. [27]  used a measurement system based on SL called OGX|4DSCANNER for real-time evaluation of lower body kinematics. A single frame pattern and a fringe projection method are employed in this system and the absolute phase distribution is reconstructed based on a single fringe with a known absolute phase value. Two-directional measurements of the lower human body can be obtained using four detectors and two pattern projectors. This system utilizes only two measurement directions, with no overlap of the projected patterns. In their research, Lenar et al. successfully demonstrated the suitability of 4D surface data for musculoskeletal analysis. However, the utilization of only two measurement directions leads to large areas with no data, which is unacceptable for most applications.