dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods An exhaustive search was conducted in order to carry out the research of papers on some of the most important and commonly used websites and scientific databases, namely, Google Scholar, IEEE Xplore  [27] , Scopus [28], Science Direct  [29] , and Web of Science  [30]. A specific search was conducted in each of them using the following words: "cloud" AND/OR "big data" AND "health" AND "security" in the title and abstract. In all cases, the time span was from 2008 to September 2017. Figure  1  shows the 3169 results obtained via the searches conducted, and we also refer to any papers that were disregarded as a result of their being duplicates or having a title that is unrelated to our area of interest. We ended up with 22 papers after having read 134 and seeing which of them proved beneficial to us after viewing the corresponding abstracts.
All the papers related to health. At the end, we decided to use these papers because after reading the others, although initially appearing to cover big data and associated security, we noted that they only mention the big data situation or the situation regarding legislation in Europe and the USA governing data on the cloud. Therefore, we decided to disregard these as they failed to provide us with relevant information, and in making our selection, we took papers written in English into consideration. After reading the titles and abstract and ending up with the 134 papers mentioned, we then proceeded to read their content and thus determine which of them would provide us with information related to big data and its security on the cloud or on databases it uses to store data.
Hingwe and Bhanu (2014) explain a database model on the cloud with architecture consisting of two additional layers, which are used depending on whether the data is sensitive or otherwise. Data encryption is added to the layers, and this takes the form of double encryption if the information is deemed sensitive. A key is needed for this encryption and a symmetric key provided by the database server is used for such purpose. Where sensitive information is concerned and hence two layers are used, this is split into two by an algorithm to improve it  [37]. Cheng et al. (  2014 ) provide a summary of the most direct threats to which a customer of a cloud supplier may be exposed. Among these threats, the suppliers themselves may use their data for their own interests or cybercriminals may acquire the data, and they propose a simple scheme to deal with these threats involving splitting the data into fragments.
Subashini and Kavitha (2011) explain a security model that does not prevent the database from being hacked but rather ensures the data obtained is of no value. They cite the example of a user's login and password in which two pieces of unrelated, separate data are of no value. Their model involves splitting the data stored into a Public Data Segment (PDS) and a Sensitive Data Segment (SDS), and SDS data needs to be fragmented still further, until each fragment does not have any value individually. This data is split using the algorithm they describe and explain in such a way that the former ceases to be of any value individually. This model is mainly focused on providing security in avoiding intrusion  [38, 40, 41]. In summary, in Table  1  a comparison of the above literature based on the most important parameters as security mechanism and problem tackled is shown.
This is because we are talking about storing data on the cloud, which is something that can be accessed via the Net and whereby disconnecting would mean not gaining access to that data or simply not being able to store it. Should we combine all these mechanisms and security algorithms? This might be one solution; it seem that things depend to quite a large extent on situations in so far as some are health-oriented; others are geared to protecting a database and others to protecting the very keys that encrypt the data being stored. One of these ways is the one we have proposed here which, although far from being perfect, may help to prevent cyber-attacks from the man-in-the-middle.
Acknowledgments This research has been partially supported by the  European Commission  and the  Ministry of Industry, Energy and Tourism  under the project  AAL-20125036  named " Wetake-Care: ICT-Based Solution for (Self-) Management of Daily Living ," by National Funding from the  Fundacão para a Ciência e a Tecnologia (FCT)  through the  UID/EEA/500008/ 2013  Project, by the  Government of the Russian Federation , Grant  074-U01 , and by  Finep , with resources from Funttel, Grant no. 01. 14. 0231. 00 ,under the  Centro de Referência em Radiocomunicacões (CRR)  project of the  Instituto Nacional de Telecomunicacões (Inatel), Brazil.
The book "Dense Sphere Packings" gives the mathematical details of the proof that was formalized  [14]. This article focuses on the components of the formalization project, rather than the mathematical details of the proof. The Flyspeck project has roughly similar size and complexity as other major formalization projects such as the Feit-Thompson odd order theorem [9], the CompCert project giving a verified C compiler  [31] , and the seL4 project that verified an operating system micro-kernel  [28]. The Flyspeck project might actually set the current record in terms of lines of code in a verification project. The code and documentation for the Flyspeck project are available at a Google code repository devoted to the project  [7]. The parts of the project that have been carried out in Isabelle are available from the Archive of Formal Proofs (afp.
sf. net). Other required software tools are Subversion (for interactions with the code repository), Isabelle/HOL  [37] , HOL Light  [21] , OCaml (the implementation language of HOL Light), the CamlP5 preprocessor (for a syntax extension to OCaml for parsing of mathematical terms), and GLPK (for linear programming)  [8]. The main statement in the Kepler conjecture can be formally verified in about five hours on a 2 GHz CPU directly from the proof scripts. As described in Section 4. 5, the proof scripts of the main statement have been saved in a recorded proof format. In this format, the formal proof of the main statement executes in about forty minutes on a 2 GHz CPU. We encourage readers of this article to make an independent verification of this main statement on their computers. This main statement reduces the Kepler conjecture to three computationally intensive subclaims that are described in Section 3.
It works by automatically using a database of theorems asserting invariance of various properties under such transformations  [22]. This is invaluable for more intricate results in geometry, where a convenient choice of frame can make the eventual algebraic form of a geometrical problem dramatically easier. The text formalization also includes more specialized results such as measures of various shapes that are particularly significant in the partitioning of space in the Flyspeck proof. Among these, for example, is the usual 'angle sum' formula for the area of a spherical triangle, or more precisely, the volume of the intersection of its conic hull and a ball. Flyspeck also uses results about special combinatorial and geometrical objects such as hypermaps and fans, and a substantial number of non-trivial results are proved about these, including in effect the Euler characteristic formula for planar graphs.
The result is a set of graphs called TameEnum. In principle we could generate TameEnum by formal proof but this would be extremely time and space consuming because of the huge number of graphs involved (see below). Therefore we rely on the ability of Isabelle/HOL to execute closed HOL formulas by translating them automatically into a functional programming language (in this case ML), running the program, and accepting the original formula as a theorem if the execution succeeds  [12]. The programming language is merely used as a fast term rewriting engine. We prove the completeness of the archive in two steps. First we prove that every tame plane graph is in TameEnum. This is a long interactive proof in Isabelle/HOL. Then we prove that every graph in TameEnum is isomorphic to some graph in the archive.
A modified dual solution can be found with informal methods. We use GLPK for solving linear programs [8] and a special C# program for finding the required modified dual solutions for linear programs. All dual solutions are also converted to integer solutions by multiplying all coefficients by a sufficiently large power of 10. The formal verification procedure uses formal integer arithmetic. There are 43,078 linear programs (after considering all possible cases). All these linear programs can be verified in about 15 hours on a 2. 4GHz computer. The verification time does not include time for generating modified dual solutions. These dual solutions need only be computed once and saved to files that are later loaded by the formal verification procedure.
In order to implement these three measures and help epidemiologists make better informed decisions about influenza alert levels, we developed a web application called MASS (Module for the Analysis of SurSaUD and Sentinelles' data). MASS compiles data from two surveillance systems in near real-time, computes statistical alarm levels and proposes data visualisations that highlight inter-regional comparisons. Although influenza was our primary target, our goal was to create a tool suitable for the monitoring of various health indicators, in particular seasonal infections. Accordingly, 11 other so-called 'syndromic groups' were also included in this project: five with (most probable) infectious origin and winter peaks (acute lower respiratory infections, bronchitis, bronchiolitis, gastroenteritis and pulmonary diseases), asthma, isolated fever, meningitis, faintness, food-poisoning and wild mushroom poisoning.
This network grew from 23 hospitals in 2004 to 650 in 2016, capturing ca 88% of all ED visits and covering all French metropolitan administrative regions. The participating hospitals transmit data to Santé publique France every day before 06:30. The files sent on a given day contain data on the patients seen during the previous 7 days. Most of the patients' information is transmitted the day following the visit. The transmitted data contain among other information: the diagnosis code according to the 10th international classification of disease (ICD-10), the consultation date and the patient's age. SOS Médecins is a federation of private emergency GP associations. In 2006 it started participating in SurSaUD with 24 associations, reaching 61 in 2016 (out of a total of 62, i. e. covering ca 95% of emergency GP consultations).
All 22 metropolitan French regions have at least one participating association. When a patient calls an association to organise a home visit, their age and primary health complaint are registered. After consultation, the doctor registers the diagnosis code on a mobile application, using a specific thesaurus. Every morning, participating associations send their data to a national platform which synthesises all data into a single file which is sent to our agency at 06:00. The transmitted information contains among other details: the SOS Médecins diagnosis code, the consultation date and the patient's age. The received data are inserted in a database. An aggregated table is automatically updated with the daily number of visits by reporting unit (hospital ED and GP association), age group (several partitions) and syndromic group.
More specifically, for ED data, the syndromic group ILI is composed of ICD-10 codes J09, J10 and J11. For SOS Médecins data, it comprises the three following codes from their own thesaurus: 'ILI or suspected influenza', 'influenza confirmed by test' and 'pandemic influenza confirmed by test'. Every morning, MASS extracts from this aggregated table the ILI data of the last 7 days corresponding to the following age groups: all ages, < 2, < 5, < 15, 2-14, 5-14, ≥ 15, 15-64, 15-74, ≥ 65, 65-74, ≥ 75, ≥ 85 years. MASS calculates, for ED and SOS Médecins data, the proportion of ILI cases among the total number of visits with a valid diagnostic code, which comprise all diagnostic types: diseases, accidents, etc. The proportion of ILI is calculated by age group, geographical area (France, metropolitan France and administrative regions), and by day, week and month.
This implies that incidence rate estimates can be compared in space and time despite differences in GPs' participation, overall numbers of GPs, and population size. However, the confidence interval of each weekly estimate depends on the number of participating GPs in the area in that week. Sentinelles has developed an R package to make the estimates of all-age incidence rates and their confidence intervals accessible to external applications such as MASS. Until February 2017, when a new version of the package was released, the incidence rates by age group were not accessible through this package, and thus were not included in MASS. The Sentinelles ILI data included in MASS are the all-age incidence rates of ILI consultation in general practice and their confidence intervals, from week 2010-W01 onwards, for the whole of metropolitan France and for all administrative regions.
Every Monday at 14:00, Sentinelles releases preliminary estimates of incidence rates for the previous week. Every Tuesday at 10:30, Sentinelles updates these estimates, based on the data reported on Monday. Besides, because the sentinel GPs can report case data up to 12 days after consultation, the incidence rate estimates are updated during the 2 weeks following their initial release. Consequently, to provide up-to-date data in MASS, the all-age incidence rates for the 3 preceding weeks and their confidence intervals were downloaded from the Sentinelles database every Monday at 14:00 and every Tuesday at 10:30.
Comparison of case characteristics Common case information reported by all data providers included age, sex and whether the cases were hospitalised following their visit to the ED or encouraged to do register at a hospital by its Sentinelles or SOS Médecins GP. These pieces of information were not available in real time from Sentinelles and were requested retrospectively at the end of the 2015/16 season. We compared the case distribution by age groups, sex and hospitalisation status to understand the specificities of each data source, as this knowledge could be useful for future refinement of MASS. The 95% confidence interval of the proportion of cases in each group was calculated using 1 -(ps × qs × rs,v) as a finite population correction factor, ps being the coverage of the data source s, qs the proportion of reported cases that have a valid diagnostic code in data source s, and rs,v the proportion of non-missing values for the variable v in data source s  [9].
Periodic regression This method has been used by many authors to predict the baseline level of an epidemiological time series in the absence of influenza, and to flag values significantly above this baseline as a possible epidemic NC: the alarm level was not computed for those combinations, which provide at most one or two results to combine. a The robust PR model applied on the Sentinelles time series failed to provide a result for 81 region-weeks. In those 81 occasions, the alarm level was based on n -1 statistical results (n being the number of statistical methods multiplied with the number of data sources). [3, 11, 12]. We modelled the baseline level with a multivariable linear regression model using the following equation: where y w is, for week w, the proportion of visits for ILI among all visits with a valid diagnosis code (for ED and SOS Médecins data), or the incidence rate for 100,000 inhabitants (for Sentinelles data).
The predicted baseline level y w in a given week w was obtained by fitting the model to the observations of the past 5 years (i. e. 261 weeks: from week w -261 to week w -1) that were below their 85% percentile. This trimming was an attempt to ensure that the dataset used to model the baseline level was free of influenza. An observation was flagged as possibly indicative of an epidemic if it exceeded the upper limit of the 95% bilateral prediction interval of the predicted baseline.
Robust periodic regression Robust periodic regression resembles periodic regression but does not require trimming  [13]. Instead, an iterative fitting algorithm allows small weights to be attributed to the highest values of the dataset (those which indicate a possible influenza epidemic). We used the same model equation and the same model training period (week w -261 to week w -1) as for the periodic regression method. Similarly, an observation was flagged as possibly indicative of an epidemic if it exceeded the upper limit of the 95% prediction interval of the predicted baseline. We employed the rlm function of the R package MASS  [14].
Web application The development of MASS started in June 2015, based on the shiny R package  [20]. At the end of July, a video demonstrating the use of the prototype was sent to 31 epidemiologists, in the regional units and at the national level, along with a web questionnaire asking for their opinions and suggestions for improvements. The answers influenced the development of MASS. The application was launched in December 2015, and refined at different moments during the following months. MASS can be accessed from within the national public health agency headquarters or from regional units, in the latter case via a secured network. The schematic process is illustrated in Figure  1. The interface is organised in tab panels. Dropdown menus and radio buttons on the left hand side can be used to filter the data to display by syndromic group, data source, age group, area, date and time step.
Acknowledgements We thank the epidemiologists of Santé publique France implicated in influenza surveillance and the partners involved in the surveillance systems described in this paper: hospital emergency department staff from the OSCOUR® network, SOS Médecins association members and the  GPs and epidemiologists of the Sentinelles network. We are also thankful to our colleague  Laurent Marie  for installing a shiny server for MASS and ensuring that it functioned adequately.
Automatic generation of the whole symmetry group The formulation group is detected automatically using the techniques discussed in  Liberti (2012) : the MILP is transformed into a Directed Acyclic Graph (DAG) encoding the incidence of variables in objective and constraints, and a graph automorphism software  (nauty McKay (1981) ) is then called on the DAG. The orbital-shrinking relaxation is constructed automatically using a mixture of bash scripting, GAP gap4 (2007), AMPL  Fourer and Gay (2002) , and  ROSE Liberti et al. (2010).
Computational Results We tested our method on the multi-activity instances used in  Côté et al. (2011a,b) ;  Quimper and Rousseau (2010). This testbed is derived from a realworld store, and contains instances with 1 to 10 working activities (each class has 10 instances). We implemented our method in C++, using IBM ILOG CPLEX 12. 2 cplex (2012) as black box MIP solver, and Gecode 3. 7. 3 Gecode Team (2012) as CP solver. All tests have been performed on a PC with an Intel Core i5 CPU running at 2. 66GHz, with 8GB of RAM (only one core was used by each process). Every method was given a time limit of 1 hour per instance. Concerning the set of feasible shifts Ω, we simply partitioned it into full-time and part-time shifts. From the implementation point of view, our hybrid method is made of the following phases: • First, the aggregated model is solved with CPLEX, using the default settings.
Computational Experiments We implemented our codes in C++, using IBM ILOG CPLEX 12. 4 cplex (2012) as black box MIP solver and Gecode 3. 7. 3 Gecode Team (2012) as CP solver. All tests have been performed on a PC with an Intel Core i5 CPU running at 2. 66GHz, with 8GB of RAM (only one CPU was used by each process). Each method was given a time limit of 1 hour per instance. In order to generate hard MKP instances, we followed the systematic study in  Pisinger (2005). More details about our instance generation procedure can be found in  Salvagnin (2013)  In order to have a reasonable test set, we considered instances with a number of items n ∈ {30, 40, 50} and number of knapsacks m ∈ {3, 4, 5, 6}. For each pair of (n, m) values, we generated 10 random instances following the procedure mentioned above, for a total of 120 instances.
Replacing the compact model with the extended model, while keeping the same solver, shows some definite improvement, increasing the number of solved instances from 110 to 115 and further reducing the running times. Note that for the instances in our testbed, the number of feasible packings was always manageable (at most a few thousands) and could always be generated by Gecode in a fraction of a second. Still, on some instances, the CP solver was not very effective in solving the feasibility model. The issue is well known in the column generation community: branching on variables x p yields highly unbalanced trees, because fixing a variable x p to a positive integer value triggers a lot of propagations, while fixing it to zero has hardly any effect. In our particular case, replacing the CP solver with a MIP solver did the trick.
Acknowledgments The first and third authors were supported by the  University of Padova  ( Progetto di Ateneo "Exploiting randomness in Mixed Integer Linear Programming "), and by  MiUR, Italy  ( PRIN  project " Mixed-Integer Nonlinear Optimization: Approaches and Applications ") The second author was partially by grants  Digiteo Chair 2009-14D "RMNCCO" and Digiteo 2009-55D  "ARM".
He can then compute backwards up to the initialization of the state and filter the correct guess on the 4w-bit constants, which subsequently reveals the 4w-bit secret key. We have successfully verified the forgery and key-recovery attacks on a toy version of NORX v2. 0, by taking the word size w = 8 and adopting the rotation constants of NORX-8. The pseudo-code of the attack can be found in app:sec:pseudocode. Adversarial Models. In a ciphertext-only setting, the adversary does not get the value of the plaintext after the decryption and cannot perform the last step of the key-recovery attack. It is however possible in chosen-plaintext or chosen-ciphertext settings. If the adversary can query a decryption oracle, he gets the value of the plaintext he needs to compute backwards and recovers the key.
2 32 ) initialization phases should be performed with the same key, but they do not give any limit to the total length of messages encrypted with a key. We can notice that if one encrypts constant 0 blocks, NORX can be viewed as a stream cipher, and therefore the Babbage-Golić [Bab95, Gol97] Time-Data tradeoff applies. In particular, NORX-8 has an internal state of only 128 bits. Therefore, if one can encrypt 2 m 2 48 message blocks under the same key with NORX-8, the security level drops below 80 bits since a state-recovery attack of time and memory complexity at most 2 128-m can be mounted, that can in turn easily be converted into a key-recovery attack using backward computations. Interpretation of the NORX Proof. Finally, we would like to raise the following problem. In the bound derived from the proof of the NORX mode of operation, the term q p σ D /2 c appears.