dataesr/scientific-paragraphs-categorization huggingface dataset:
Since the detection requires high-frequency polling of performance counters, applying it system-wide could cause significant slowdowns and lead to false positives, so it should ideally be run by the user himself on the sensitive process he wants to monitor against attacks (strongSwan would be a natural candidate!). An additional issue worty of mention is that, perhaps due to the fact that perf_events are not always perceived as a possible attack vector, the relevant part of the Linux kernel does not seem to be the most scrutinized from a security standpoint. In particular, until May 2016, a race condition in perf_event_open allowed to bypass the privilege verification entirely  [5] , which means that our attack could be conducted from any userland process (and probably still can on many live systems).
Recovering the BLISS norm in strongSwan with branch tracing. The strongSwan implementation of BLISS signature generation uses a direct implementation of the Bernoulli-based algorithm for  rejection sampling described in Figure  2. In particular, the function that implements SampleBernExp, called bernoulli_exp in strongSwan, iterates over each bit of the input value x, and skips to the next iteration whenever the bit is zero. More precisely, branches are taken in bernoulli_exp: (1) to enter into the function; (2) to iterate over the bits of the input x; (3) each time the corresponding bit is 0; (4) to return from the function. If we can find the addresses corresponding to all of these branching instructions, the information provided by perf script will immediately reveal every bit of x, which will let us reconstruct x and hence mount the attack described in §3.
2. The strongSwan software (and more generally any program linked against the libstrongswan library) accesses the BLISS-related functions from the libstrongswan-bliss. so object, which is dynamically loaded using dlopen(3). So to find the addresses of relevant branching instructions in the memory space of the attacked process, it suffices to find the offsets of those instructions within libstrongswan-bliss. so, as well as the address at which that library is mapped in the memory space of the process once it is loaded. The latter can be done by parsing the line corresponding to the libstrongswan-bliss. so executable segment in the file /dev/⟨pid⟩/maps. As for the former, it can be done using a disassembler, such as the disassemble/m command in gdb, as shown in Figure  7. We can see from that figure that, in our compiled version of the libstrongswan-bliss.
so object, the four offsets we are interested in are respectively 0x66f0 (entry point), 0x67c6 (while loop iteration), 0x674d (conditional branch on the bits of x) and 0x67ad (return). The same method allows us to find the address in the BLISS signing function sign_bliss at which the bernoulli_exp method is called (it can be either 0x2de3 or 0x2f51). Then, it suffices to find the corresponding branching event in the output of perf script and follow from there the branching instructions carried out inside bernoulli_exp to recover x and hence ∥Sc∥ 2. The entire attack has been mounted against a short program linked against libstrongswan-bliss. so that generates a BLISS key, computes a BLISS signature with it and verifies it. A short shell script runs that program, launches perf report on it, and calls a perl script to parse the output of perf script afterwards.
An example output of the shell script implementing the attack is shown in Figure  8. We are able to recover the value ∥Sc∥ 2 correctly all the time. The source code of the entire attack is available for download from https://github. com/mti/bliss-sidechannel. Features and limitations of the attack. The adversarial use of perf_events-based branch tracing does not seem to have received much consideration in the literature, but it is quite similar to the branch prediction attacks of Acıiçmez et al. [1] [2] [3]. The branch tracing side-channel is more robust, however: on most CPUs, branch prediction is randomized or at least non-deterministic, so that the information one can get from branch mispredictions and similar events is noisy. Our attack, on the other hand, produces a complete and faithful execution trace, and thus succeeds all the time.
It does shares some of the limitations of branch prediction attacks, however. In particular, the attack model is quite strong, as it requires the ability to run another process on the same platform, usually with the same UID as the target process (unless privilege checks can be bypassed as in the perf_event_open security bug mentioned earlier). As a result, the threat posed by this kind of attack is normally limited. It cannot be ignored entirely, however, as modern Linux systems, through security measures like ptrace protection, will not allow a process to e. g. read from the memory space of another process from the same user: userland interprocess spying is usually regarded as a significant security issue. Similarly, secret keys stored on disk are usually encrypted and passphraseprotected, so one cannot assume that a process can learn all of a user's secrets just by acquiring his UID.
Simulating the Hamming weight of 16-bit variables, correct constraint systems were obtained up to noise standard deviation 1. 5 (as expected for a Gaussian noise). Using such error-less systems and the Gurobi ILP solver  [29]  the secret vectors were recovered within a few dozen of milliseconds. To handle higher levels of noise, the attacker should discard constraints in which he has less confidence (i. e. those corresponding to leakage close to the cut-off). Simulations have been performed up to a noise of standard deviation 3. 5 on a desktop computer. Results are given in Table  4. They were obtained on a set of 50 systems for each noise level and they show that attacks are easily performed in this range. We see that the solver should be asked to look for more than a single solution in some settings.
DISCUSSION We have presented side-channel attacks against three parts of the BLISS signing algorithm, namely the rejection sampling step, the Gaussian sampling, and the multiplication of the secret key by the hash value c. We have found them to yield full key recoveries, either on the embedded 8-bit AVR implementation of Pöppelmann et al. [46]  through EM emanations, on in the strongSwan software  [51]  on a Linux desktop machine under branch tracing. In this section, we n discuss possible countermeasures and conclusions that one could draw from these observations.
Precisely, it is to find solution that combines sustainable solutions and the advantages of ICT tools in order to realize a complete and autonomous system for water and power supply, with a particular emphasize on saving energy. This involves the establishment of appropriate pumping mechanisms and an efficient process of switching between energy sources. The smart system should guarantee the continuous providing of water to households, and automatically switch between several water and power resources according to the source availability and the service costs. For ICT tools applied on the automation applications, International Electrotechnical Commission (IEC) has issued five standard programming languages defined in the IEC 6331-3 standard  [9]. Among them, Grafcet (or SFC) is a powerful graphical language useful for both specification and programming of industrial automation systems  [5].
It is nowadays considered as one of the most used specification languages of IEC standard. Many research works have been carried out to promote and popularize this language, and many modeling and simulation tools as UniSim (  [1, 8] ), PLCopen XML  [13] , Isagraf,. are based on Grafcet standard. On the other hand, the Grafcet language has actually been built and developed to be integrated in the design and implementation software for programmable logic controllers (PLC) devices (  [1, 4, 12] ). PLC are digital devices basically used to control automated electro-mechanical systems. They are specially designed to survive in harsh industrial situations and could not be recommended for other simpler control applications. Hence, they are relatively expensive for developing countries and are not required to be operated in corresponding environments.
Our investigations on Grafcet synthesis have permitted to carry out a novel software tool that permits to automatically convert any Grafcet specification code to a control program deployable on very economic microcontrollers. This tool is presented and can be exploited to generate the appropriate solution once the system is well specified in Grafcet language. The paper is organized as follows: Section 2 presents the problem to be solved with different configurations; the proposed model is explained in Section 3. Section 4 presents the study of the logic controller modelling using Grafcet and a solution synthesis approach to obtain an automatic control system. Section 5 draws a case study with the corresponding Grafcet model and conclusions are drawn in Section 6.
Logic Control using Grafcet From the physical layout architectural water supply system (as shown in Figure  3 ), we can describe several functional requirements of systems considered in particular contexts. In addition to the case of the whole system, some interesting systems may be obtained considering the fact that there is the absence of one of the following functions: urban electricity supply, drilling/well, urban water supply or battery. In this section, our attention is focused on the whole system from which the solution to other systems cases can be obtained. Initially, let's look at an overview of the Grafcet modeling language. Next, we will propose pieces of grafcet to model the basic functionalities of this system and finish it with the presentation of the synthesis solution strategy.
The Grafcet modeling language The Grafcet (Graphe Fonctionnel de Commandes et d'Etapes/Transition), a graphic language for modeling automated systems, is the most international formalism used for high-level description of complex sequential systems  [5]. It is widely used in several domains such as home automation, automotive, power generation, manufacturing, etc. A grafcet specifies the behavior of a logic control system. This language has been standardized by the International Electrotechnical Commission IEC 60848  [3] , and is understandable and readable compared to other specification languages as Petri net, Relay ladder, Instruction list, etc. A grafcet (program written in Grafcet language) is a graph that consists of two types of nodes: steps and transitions. A step is represented by a square, while a transition is represented by a horizontal line.
Synthesis solution strategy Given the technical design specifications of the water supply system, the grafcet specification model is derived from. We present in this subsection the synthesis process for logical control systems specified in Grafcet. A grafcet model can be realized on programmable controllers (such as microcontrollers or PLCs). However, they do not allow a direct implementation on control targets like microcontrollers, which cost comparatively less than PLCs. For this, we have conducted research on the automatic and robust synthesis of logic controllers specified in Grafcet language and realized on microcontroller targets. A program synthesis tool is then designed for microcontrollers, according to the Grafcet algebraic equations approach broadly presented in sub-section 4. 4.
-Step 2. Grafcet model : The next step is to represent the technical specification of the system by Grafcet model. We propose UniSim software tool in order to edit Grafcet chart  [8]. It is a graphical user interface (GUI) that offers gallery of specific graphical objects (shapes) for all Grafcet elements (such as steps, transitions, transition-conditions and actions). The user can easily select these graphical objects, place them onto a drawing page and attach them together in order to create specific grafcet charts. Furthermore, UniSim tool permits to reformat or serialize Grafcet data into XML format  [13] , this option ensures that this information can be easily processed by another software application (interoperability property), especially the one that we have realized. -Step 3. Graf cetConverter tool : We have built this software tool in order to directly read and extract the Grafcet data from XML format file and generate the appropriate program according to the Grafcet algebraic equations approach presented in subsection 4.
4. This program in C language is ready to be compile by the appropriate compiler to build the corresponding binary program. Furthermore, Graf cetConverter tool provides a simulation engine with which we can simulate the Grafcet program and verify whether it meets the requirements or not before generating the C-program for the selected microcontroller. This tool can generate code for microcontrollers, especially the EASYdsPIC4 of the Microship dsPIC family  [14]. -Step 4. Integrated Circuit : We are using the EASYdsPIC4A microcontroller, a full-feature and very powerful development system for PIC microcontrollers from Microchip  [14]  that holds a dsPIC30F4013 microprocessor. In C language, we have implemented the Grafcet interpretation algorithm  [5]  using the MPLAB IDE. The board is programed by generating and transferring a binary program to the EEPROM board through USB cable or RS-232 COM port.
Grafcet implementation with algebraic equations In the program outputted by the synthesis tool, to make sure that the evolution of the Grafcet situation is done according to the Grafcet evolution rules, we automatically generate Grafcet algebraic equations as stated by J. Machado et al in  [2]. They use algebraic equations as a common model for simulation and formal verification to obtain a safe controller specification that will be implemented on PLC. This control program runs according to a scan-based perception which is an effect of the PLC cyclic mode of operation. Figure  10  presents the scan-based cycle processed in every step of the microcontroller (µC) during its sequential execution. The Grafcet algebraic equations generated when producing a control program are defined as follows: Let CC(tr) (Clearing Condition) be a Boolean variable associated to every transition tr of a Grafcet.
Grafcet specification model for the system From functional specifications of the water supply system described in the previous subsection, we derive the corresponding Grafcet specification model, according to the general method described in subsection 4. 2. The Grafcet specification of the functioning of this logic control system is shown on the following figures : Figure  13  and Figure  14. As described in the synthesis process in 4. 3, when the control program generated from the Grafcet specification model of the water supply system is compiled an sent to the EASYdsPIC4A board, this board serves as the core of the smart control unit. Thus, it receives input signals from the sensors (presence of water, pressure, and electric power), analyzes them and generates output signals in form of commands or actions to perform specific acts (open/close valves, turn on/off the pumps or choose a power source).
For a recent application and further references see  Bonat and Jørgensen (2016). The rest of the paper is organized as follows. In the next section, we provide some background about Tweedie regression models. Section 3 discusses the approaches to estimation and inference. Section 4 presents the main results from our simulation studies. Section 5 presents the application of Tweedie regression models to three data sets. The first one concerns daily precipitation in Curitiba, Paraná State, Brazil. This dataset illustrates the analysis of positive continuous data with probability mass at zero. The second data set corresponds to a cross-section study developed for studying the income dynamics in Australia. This dataset shows the analysis of positive, highly right-skewed response variable. The last data set illustrates the analysis of symmetric positive data, where current implementations have problems to deal with power parameter smaller than 1.
The algorithm is implemented in the package tweedie  (Dunn; 2013)  for the statistical software R(R Core Team; 2016) through the function dtweedie. series. Dunn and Smyth (2008)  also studied two alternative methods to evaluate the density function of the Tweedie distributions, one based on the inversion of cumulant generating function using the Fourier inversion and the sandlepoint approximation, for more details see  Dunn (2013). In this paper, we used only the approach described in this Section, i. e. based on series expansions. We now turn to Tweedie regression models. Consider a cross-sectional dataset, (y i , x i ), i = 1,. , n, where y i 's are i. i. d. realizations of Y i according to Y i ∼ Tw p (µ i , φ) and g(µ i ) = η i = x i β, where x i and β are (Q × 1) vectors of known covariates and unknown regression parameters, respectively.
(4) The entry (j, k) of the 2 × 2 Fisher information matrix F λ for the dispersion parameters is given by F λ jk = -E ∂ 2 L(θ) ∂λ j ∂λ k. (5) The derivative in equations (  3 ), (  4 ) and (  5 ) depends on the derivative of the infinite sum a(y i ; λ), and it cannot be expressed in closed form. Hence, numerical methods are required for approximating these derivatives. Let Ũλ and Fλ denote the approximated score function and observed information matrix for the dispersion parameters, respectively. In this paper, we adopted the Richardson method  (Fornberg and Sloan; 1994) , as implemented in the R package numDeriv  (Gilbert and Varadhan; 2015)  for computing these approximations. Furthermore, the cross entries of the Fisher information matrix are given by F β j δ = -E ∂U β j (β, λ) ∂δ = -E µ i x ij - 1 exp(δ)µ p i (y i -µ i ) = 0 and F β j p = -E ∂U β j (β, λ) ∂p = -E µ i x ij ∂ ∂p 1 exp(δ)µ p i (y i -µ i ) = 0.
The mean structure was specified as in the subsection 4. 1. In the case of heavy tailed distributions, we expect negative values for the power parameter. Thus, we fitted the Tweedie regression models by using the quasi-and pseudo-likelihood approaches. In order to compute the empirical efficiency of the quasi-and pseudo-likelihood estimators, we fitted t-Student regression models along with the logarithm link function, as implemented in the package gamlss(family TF)  (Rigby and Stasinopoulos; 2005). Although, of the extensive literature on robust estimation methods, in this paper we adopted the t-Student regression models, since it is a frequent choice for the analysis of heavy tailed data  (Huber and Ronchetti; 2009)  and can be fitted using the orthodox maximum likelihood method. Furthermore, since there is no software available for fitting slash regression models using logarithm link function, the t-Student q q q q beta[0] 1.
Smoothing time series of rainfall in Curitiba, Paraná, Brazil This example concerns daily rainfall data in Curitiba, Paraná State, Brazil. The data were collected for the period from 2010 to 2015 corresponding to 2191 days. The main goal is to smooth the time series to help us better see patterns or trends. The analysis of rainfall data is in general challenged by the presence of many zeroes and the highly right-skewed distribution of the data. The plots shown in Fig. 7  illustrate some of these features for the Curitiba rainfall data. In particular, Fig. 7 (B) highlights the right-skewed distribution and the considerable proportion of exact 0s (51%). In order to smooth the Curitiba rainfall time series, we fitted a Tweedie regression model with linear predictor expressed in terms of  B-splines (de Boor; 1972).
Regarding the dispersion parameters, although the slightly difference in terms of estimates and standard errors, the confidence intervals from the QMLE and PMLE approaches contain the MLE estimates. Concerning the effect of the covariates the MLE and QMLE approaches agree that the covariates weeks and south are non-significant. On the other hand, the PMLE approach also indicated that the covariates industry and married are nonsignificant. Regarding the other covariates the three approaches agree that they are significant. In order to compare the fit of Tweedie regression model with more standard approaches, we also fitted the Gaussian, gamma and inverse Gaussian regression models for the income dynamics data set. The maximized values of the log-likelihood function were -4437. 51, -4318. 08 and -4316.
Gain in weight of rats The third example concerns to a standard Gaussian regression model. The goal of this example is to show that the quasi-and pseudo-likelihood approaches can estimate values of the power parameter between 0 and 1, where the maximum likelihood estimator does not exist. We used the weightgain data set available in the HSAUR package  (Everitt and Hothorn; 2015). This data set corresponds to an experiment to study the gain in weight of rats fed on four different diets, distinguished by the amount of protein (low and high) and by source of protein (beef and cereal). The data set has 40 observations. We fitted the Gaussian, gamma, inverse Gaussian and Tweedie regression models for the weightgain data set. The linear predictor was composed of the two main covariates source and type along with the interaction term, for all models.
For tropospheric-only chemistry, the CMIP6-specified stratospheric ozone dataset should be used  (Hegglin et al. , 2017). Modelling groups with full chemistry and aerosol models are encouraged to perform all simulations they deem relevant to their objectives. Note that, for consistency, the concentrations of chemically and radiatively active species should be the same in the radiation and chemistry schemes. Tables  1 to  6  list the minimum model configurations required for each experiment. The suffix "CHEM T " or "CHEM S " indicates interactive tropospheric or stratospheric chemistry is the minimum needed for these experiments. The suffix "AER" indicates that interactive aerosols are needed. For models without interactive tropospheric chemistry, the Tier 1. NTCF and Tier 2. Aer simulations will be identical, and so only need to be run once.
(2017)  (http:// www. globalchange. umd. edu/ceds/ceds-cmip6-data/). Models should use their own schemes for natural emissions. The WMGHGs (methane, nitrous oxide, halocarbons) will be specified as CMIP-specified concentrations, either throughout the troposphere or at the surface. We also realise that valuable contributions to answering the AerChemMIP scientific questions can be made by groups unable to participate in CMIP6, such as those running offline CTMs. Participation from these groups is welcomed and encouraged in the wider Chemistry-Climate Model Initiative (CCMI;  Eyring et al. , 2013b)  and AeroCom projects, but the data will not form part of the official CMIP6 submission. We have arranged the experiments into three tiers to reflect their priority. Tier 1 experiments are those necessary to answer science questions 1 and 2 in terms of overall impacts of NTCFs and reactive well-mixed gases.
Weak pollution controls assume delays to the implementation of CLE and make less progress towards MTFR than the medium scenario. For more details, see  Rao et al. (2017). To detect the largest signal we choose the reference scenario to be SSP3-7. 0 "Regional Rivalry" without climate policy (7. 0 W m -2 at 2100, experiment ssp370), see  Fujimori et al. (2017) , as this has the highest levels of short-lived climate pollutants and "Weak" levels of air quality control measures  (O'Neill et al. , 2016; Rao et al. , 2017). The ssp370 ScenarioMIP simulation will need to have been run with the AerChemMIP setup and diagnostics, or repeated here. The data for the perturbation experiment to this within AerChem-MIP will be generated by the Asia-Pacific Integrated Modeling (AIM) group using the same socio-economic scenario as in Fujimori et al.