dataesr/scientific-paragraphs-categorization huggingface dataset:
Here we note that, informal agreements based exclusively on mutual and collaborative efforts, as carried out here with Università di Torino, are developed during an eruptive crisis. However, as discussed later, continuity of service provision, data validation efforts, transparency and efficiency then benefit from development of more robust, formal agreements developed during non-eruptive periods. During and between crises the role of OVPF is to communicate with the national, regional and local responding agencies. Following the French national plan for response protocols during a crisis at Piton de la Fournaise, the call-down procedure is laid out in "Organisation de la Réponse de SEcurité Civile" (ORSEC), i. e. , the "ORSEC-Piton de la Fournaise" plan. Within this plan OVPF communicates only with the prefecture via the "Etat Major de Zone et de Protection Civile de Ocean Indien" (EMZPCOI).
univ-bpclermont. fr/SO/ televolc/dynvolc/; GazVolc: Observation des gaz volcaniques, http://wwwobs. univ-bpclermont. fr/SO/televolc/gaz volc/). International collaboration with the INGV in Pisa and the Università di Torino also allows near-real time provision of potential lava flow paths and validated timeaveraged lava discharge rate (TADR), respectively. These products have been coupled with operational lava flow modelling at LMV to allow assessment of potential lava flow run-out. Collaboration with Chalmers University (Gothenburg, Sweden) is fundamental for post-processing and validation of SO 2 flux data acquired by the permanent NOVAC DOAS network. The Network for Observation of Volcanic and Atmospheric Change (NOVAC) is a system of automatic gas emission monitoring at active volcanoes using a worldwide array of permanently-installed differential optical absorption spectroscopy (DOAS) scanners which measure volcanic gas emissions by UV absorption spectroscopy  (Galle et al.
5 ). MODIS is a multispectral radiometer carried aboard the Terra (EOS-AM) and Aqua (EOS-PM) polar orbiting satellites. MODIS acquires data of the entire Earth's surface in 36 wavebands and offers a temporal coverage of ∼4 images per day at a spatial resolution of 1 km in the infrared (IR) bands, specifically bands 21 and 22 (3. 929-3. 989 μm, low and high gain, respectively), 31 (10. 78-11. 28 μm) and 32 (11. 77-12. 27 μm). Using MODIS, MIROVA completes automatic detection and location of high-temperature thermal anomalies, and provides a quantification of the volcanic radiant power (VRP), within 1 to 4 h of each satellite overpass  (Coppola et al. 2016). With each overpass, thermal maps (in. kmz format for use with Google Earth) and VRP time-series are updated on the MIROVA website (www.
1  for locations). Wind speed data are acquired hourly by an anemometer installed on a mast 10 m above the ground. Post-processing is carried out in collaboration with Chalmers University and allows SO 2 flux to be correlated with daily rain fall data, acquired by the OVPF stations, to farther constrain the environmental effects on the gas flux estimates. While the short-lived June 2014 and July-August 2015 eruptions occurred during good weather conditions, periods of rain interrupted the longer eruptions of May 2015 and August-October 2015. Bad weather conditions were dominant during the February 2015 eruption, making any real time assessment of gas emissions unreliable during most of the event. Precise assessment of gas fluxes is also challenging during very short-lived eruptions (lasting just a few hours), especially if a significant part of the eruption occurs at night when the UV-reliant DOAS acquisition cannot be performed.
Results In total 127 TADR values were derived and delivered to OVPF by the MIROVA system during the 2014-2015 crises, cloud cover meaning that reporting varied between zero and four TADRs per day; with an average of two per day during the August-October 2015 eruption. During the five 2014-2015 eruptions, the results of 31 sample analyses, plus all DOWNFLOW and FLOWGO model runs were delivered directly to OVPF. All measurements and products were cross-checked, and merged with data fed back into the communication loop by the monitoring system operated by OVPF. This allowed source term update, uncertainty control and validation, as well as fully constrained event tracking.
In this case the main causes for concern are (i) burial of the island belt road (RN2), (ii) destruction of observatory instrumentation, (iii) evacuation of the Enclos Fouqué caldera, (iv) security of the footpaths, (v) injury to (and evacuation of ) tourists, (vi) fatalities among tourists entering the closed zone, and (vii) forest fires ignited by the active lava. For an eruption in inhabited areas outside of the Enclos Fouqué caldera, including densely populated areas such as around Le Tampon (Fig. 1a ), the impact and response model would have to be scaled up, other external participants called, and the component models adjusted to suit, and tested and validated on, the new case. We are currently preparing for such an eventuality through an initiative entitled ANR-LAVA (Lava Advance in Vulnerable Areas).
Acknowledgments We thank two anonymous reviews for their input, and the patience of the Applied Volcanology Editors Office for allowing us two extensions as we were distracted by two eruptive crisis during the submission (31 January -5 February 2017) and correction (18-19 May 2017) of this manuscript, respectively. We thank the  STRAP  project funded by the  Agence Nationale de la Recherche  ( ANR-14-CE03-0004-04 ) and the  OMNCG OSU program  from  la Reunion University  for supporting DOAS data acquisition and processing. This work was funded by the  Agence National de la Recherche  (ANR: www. agence-nationale-recherche. fr) through project  ANR-LAVA  ( ANR Program :  DS0902 2016 ; Project:  ANR-16 CE39-0009 , PI:  A. Harris , Link: www. agence-nationale-recherche. fr/?Projec-t=ANR- 16-CE39-0009 ).
Acknowledgements We are grateful to  Fabrice Gamboa , who initiated this research. We thank  EDF R&D/LNHE  for providing the Mascaret test case and  Sébastien Petit  who has performed the computations on this model. We also thank  Laurence Grammont  and the members of the team ' Génie Mathématique & Industriel ' for useful discussions. We acknowledge the participants of MascotNum 2016 & Mexico Mas-cotNum conferences for their feedbacks. Part of this research was conducted within the frame of the Chair in Applied Mathematics OQUAIDO, gathering partners in technological research ( BRGM, CEA, IFPEN, IRSN, Safran, Storengy ) and academia ( CNRS, Ecole Centrale de Lyon, Mines Saint-Etienne, University of Grenoble, University of Nice, University of Toulouse ) around advanced methods for Computer Experiments.
The present project In the present project we aimed at evaluating the influence of emotional content of pictures on our ability to inhabit a digital environment, to experience and to memorize information. Our questions in this experiment were: Does this faculty of empathy apply when two subjects share the same space at a distance, through avatars? Does this co-presenceoutside of our physical bodies -, have an impact on the quality of perception of the emotional content of picture in the digital space? What kind of information should we most likely memorize? To attempt to answer these questions, we have built-up a multidisciplinary team associating, Francois Garnier, Film director and teacher researcher in Art and  communication, head  Program "Investissements d'avenir» (ANR-10-IDEX-0001-02 PSL") This research is part of an "Art and Science" approach.
It is based on the observation of the practices of communities of players, users or artists. In response to questionings regarding specific practices, we develop screenplays in digital paradigms and study the behaviors and emotional responses of the human subjects in immersion. This approach requires that we develop technologies, define experimental protocols. We need to set up and validate data capture and analysis devices (proxemics, biometric, behavioral and psychological analysis). In this first experience, our hypothesis is copresence through avatars, of human subjects physically distant, in a tridimensional digital space online, influences either positively or negatively the perception and memorization of information. We asked 51 female subjects to visit a virtual art exhibition as if they were members of a jury critic.
Physiological data inform us about the behavior and the emotive state of the subject. The subjects also answered a questionnaire at the end of the visit concerning the pictures that they had seen to test their memory. The study of these data should enable us to measure if the presence of the other avatar had an impact on the subject. The results will allow us to contemplate new experiments to refine our understanding of the cognitive and psychological modalities implemented in these situations of social interactions in virtual worlds and to consider their applications.
The management of the databases recording was easy. The paradigm has been modeled in Blender by Donatien Aubert. The entire management of interactions, interfaces and databases was carried out in Unity by Dimitrios Batras, Dionysis Zamplaras. The platform implemented during the project has proven to be flexible to adapt during the tests, and technically stable during the experiment. We did not encounter major technical problems. Only one person in fifty one subjects stopped before completion of the visit. She was over-dressed and suffered from the heat.
Behavioral and physiological data. During the visits the following data were recorded: -Proxemics, record of movements and direction of gaze of the avatar of the subject -Direction, distance and duration of the gaze, relative to the other avatars and to the works of art. -Physiological data: heart rate, blood pressure, and cutaneous conductance. -Behavioral data: trunk and head movements of the subject (Kinect). -Video recording of the subject (See Figure  4a ) and Video recording of screen view, third Person Point Of View. (See Figure  4b ).
We also recorded a video from the point of view of the subject in the paradigm, including his avatar (POW 3rd person). The biological and behavioral measurements of the subject in the cabin. We have recorded, heart rate, skin conductance, bust and head movements of the subject and a subject video during the visit. On the basis of these raw data, we have developed two study strategies: -A statistical approach, based on the data of all the subjects studied, according to the conditions S-AA-PA -A subject-based approach, based on viewing the recorded videos and analyzing the visit graph. These graphs gather all the recorded data, allowing us to identify and study specific behaviors.
Proxemic measures: We have recorded the initial conditions of the paradigm, the geometry of the rooms, the respective positions of the art works exhibited randomly in the rooms, as well as the trajectories of displacement and orientations of the avatars in the paradigm. This data was reliable as it was directly recorded digitally via Unity. It was necessary to exclude certain periods of registration when subjects were hindered by external events during their visit. (eg dressing problems, intervention of the manipulator on a technical problem, etc. ) Based on these data, we calculated proxemic values: the distance between the different avatars in the rooms, the distance of the subject avatar from the works, and we measured the viewing time of each work. These data were then studied according to the conditions S-AA-PA with the aim of detecting a possible influence of the copresence on the behavior of the subjects.
Trunk and head movements. Movements detected within a pre-defined triangle, from shoulders to the subject's head, were measured using a Kinect during manipulation. The curves of these movements inform us about the rate of activity of the trunk and head of the subject. This measure was technically reliable. Particular attention must be paid to the proper functioning of the tracking during the start of the visit. The main difficulty encountered is to differentiate the movements directly related to the activity of the subject's avatar in the paradigm, from those due to real external disturbances that interfere with the subject in the cabin. We have identified the periods of overactivity of the subject, and we have determined the exact origin of it by viewing the videos. Since the origin of the activity was clearly related to external perturbations and not to the action within the paradigm, we excluded them from the data to be analyzed.
Cutaneous conductance. Changes in Cutaneous conductance are proved indicators for detecting changes in the subject's emotional state. These data were recorded using a sensor on the index and the middle finger of the subject's left hand. The amplitude and the quality of the signal obtained varies greatly according to the subjects. It is difficult to automatically detect curve variations for statistical analysis. Significant signal variations were detected visually. A verification by viewing the videos was done to identify their origins by checking whether they were related to an event affecting the avatar of the subject in the paradigm, or the subject in the cabin. (See Figure  11 ) The variations of cutaneous conductance were valuable indicators for the detection and the study of specific situations.
Case studies. In this approach, the focus was on the behavior of a subject during his visit. we seek to identify specific strategies or situations that can inform us about the influence of co-presence on the subject's actions. We visually gathered all the data collected in the form of a temporal graph of the visit, which we synchronized with the video recording of the subject in the cabin, and the video record from the point of view of the subject on the paradigm. (See Figure  13 ) The graph shows x time (identical to that of the videos), and y the distance traveled by the subject during the visit. The S, PA and AA conditions are identified by a color code Green Red Blue. Dotted lines mark the passages from one room to another. The time of gaze on each artwork is materialized by a column, on which is stipulated the reference of the artwork.
All mentioned approaches are designed for the Hadoop infrastructure and utilize Kerberos secure resource sharing protocol. In the theoretical field it is possible to mention some works related to the concept of access control policy  [8] , hybrid environment machine-learning systems  [9]  among other, but there are no general analytical articles related to Big Data security. The existing approaches to securing the Big Data processing systems are designed and implemented as patches, i. e. , adjusted to the current needs of the system. The Big Data security theory lacks the formal conceptual models of the consistently secure Big Data processing systems, which obviously leads to the absence of the verifiable solutions for security  [10]  and  [11]. The top 10 security problems in the Big Data cloud processing are mentioned in the CSA (Cloud security alliance) paper  [12].
Fast-Sec presents a proposal to address one of the most demanding of these problems, i. e. , the problem of access control and which is related to the user privacy protection. The proposal presents a novel approach to the Big Data access control implementation. The methodology is based on the specific access control methods designed for the Big Data processing incorporated into the cloud infrastructure. The proposal includes a framework called Small & Medium sized Enterprise Data Analytic in Real Time (SMART)  [13]  and related access control methods that support and protect the data analytic processes and associated access policy. SMART is a modular framework for Big Data analysis that takes advantage of cloud, multiple clouds or hybrid infrastructures to provide support for Small & Medium-sized services operation.
The Apache Flink, formerly known as Stratosphere  [30] , is the base infrastructure of the SMART framework system, represented in Figure  2. Its flexible pipeline enables several MapReduce and extended functions like Map, Map-Partion, Reduce, Aggregate, Join and Iterative. It can be used in order to allow this cloud extension. The setting will be transparent to users because a middleware in a top level abstracts the complexity away from the users. The different layers of the stack build on top of each other and raise the abstraction level of the program representations they accept: • The API layer implements multiple APIs that create operator DAGs for their programs. Each API needs to provide utilities (serializers, comparators) that describe the interaction between its data types and the runtime.
The concrete types and their interaction with the runtime are specified by the higher layers. • The Flink Runtime layer receives a program in the form of a JobGraph. A JobGraph is a generic parallel data flow with arbitrary tasks that consume and produce data streams. The runtime is designed to perform very well both in setups with abundant memory and in setups where memory is scarce. Flink explores the power of massively parallel computing for advanced analytics and leverages a novel, database inspired approach to analyze, aggregate, and query very large collections of either textual or (semi-)structured data on a virtualized, massively parallel cluster architecture. It combines the strengths of MapReduce/Hadoop with powerful programming abstractions in Java and Scala and a high performance runtime.
In addition to basic data flow concepts as common in relational databases, or systems like Hadoop and Spark, Flink has native support for iterations, incremental iterations, and programs consisting of large DAGs (Directed Acyclic Graph) of operations. Particularly, it is possible to highlight the following: • Flink uses a richer set of primitives than MapReduce, including primitives that allow the easy specification, automatic optimization, and efficient execution of joins. This makes the system a more attractive compilation platform for data warehousing, information extraction, information integration, and many other applications. The programmer does not need to worry about writing parallel code or hand-picking a join order. • Flink includes native support rather than outside loop in Mahout  [31]  on top of Hadoop for iterative programs that make repeated passes over a data set updating a model until they converge to a solution.
Authentication and Access Control Mechanisms In order to address the issues related to authentication and access control, the proposed approach consists in an infrastructure including the following components: Encryption server; File storage; Key storage; and Client UI. These elements are illustrated in Figure  3  and detailed in the following. Encryption server manages all the certified authentication (CA) and encryption operations and grants the user access to the data storage. This server can store the encryption keys and/or connect to a separate Key Storage server. File storage is secure in the sense that some of the files specified by the domain administrator are store, encrypted and have restricted access. Due to the fact that the file storage data is partially stored in the cloud, i.
Additionally, in order to increase the security of the sensitive data we propose the following methods: a. Set up the key expiry period; b. Use separate key for the different files; c. Use secret sharing mechanism to key storing with the most sensitive data. Encryption server: The most important cryptography services are run on the Encryption server. This server generates the user keys and connects to the client UI, i. e. , a separate user of the system and decides whether the access to the specific dataset should be granted to this user. In addition, the server runs the key renewal routines, stores the user public keys and attributes besides the auditing data. Client UI: The client UI connects to the encryption server and checks the expiry period of the user keys (in the case it has been configured) and permits the device user to view/edit/upload the data.
Client UI stores the user keys for the ABE encryption and the unique symmetric session keys which serve for restricting the access to the downloaded files. The symmetric keys are encrypted with the ABE keys. The client allows the whole system to work in the heterogeneous environment as it supports different platforms and operating systems. The described modular infrastructure allows setting up different components separately and configuring the security system according to specific needs. The integration of the security infrastructure into the Big Data environment is possible due to its flexibility and scalable architecture. The main purpose of the proposed security infrastructure is the setup of the access control in the cloud-based protected environment. The access control mechanism performs tasks as follows: (1) Authentication of users in the cloud system: The initial authentication is performed by user password and email id.
The encrypted cloud storage is also separated. The whole ecosystem is shown in Figure  6. This integration model makes it possible to implement the required level of security and user privacy in all types of governmental and corporation organizations and services (like medical or governmental services) using the SMART infrastructure. The modular implementation design allows protecting the user privacy and control the access to the sensitive data in the heterogeneous environment via the Client UI developed for various OS and various platforms. The security is globally supervised with the help of the Encryption server and separate key storage. Additionally, this architecture can be easily extended in order to use more sophisticated methods of ensuring the data and key protection, i. e. , secret sharing or complex attribute policies.
To perform this selective encryption, a given number of files, or fields in the database, are not encrypted while others are encrypted and sent over the insecure network (in the cloud). This concept avoids network overload and allows controlling the security of the files. The control of this process is performed by the domain manager access control utility. By its turn, the client controls security from the user side and in the cloud. This control is implemented on different platforms, according to the specific control that is under concern. The proposal supports the concept of light-weighted client, i. e. , it the client side is not responsible for executing much encryption or other critical/heavy operations. All encryption-heavy operations are performed on the server side, while the client just supports the key usage.
The identification of non-verbal emotional signals, and especially of facial expressions, is essential for successful social communication among humans. Previous research has reported an age-related decline in facial emotion identification, and argued for socio-emotional or aging-brain model explanations. However, more perceptual differences in the gaze strategies that accompany facial emotional processing with advancing age have been under-explored yet. In this study, 22 young (22. 2 years) and 22 older (70. 4 years) adults were instructed to look at basic facial expressions while their gaze movements were recorded by an eye-tracker. Participants were then asked to identify each emotion, and the unbiased hit rate was applied as performance measure. Gaze data were first analyzed using traditional measures of fixations over two preferential regions of the face (upper and lower areas) for each emotion.