dataesr/scientific-paragraphs-categorization huggingface dataset:
Crowdsourcing: tasks and results This section addresses the following research questions: RQ2 -Do humans succeed in detecting social spambots in the wild? RQ3 -Do they succeed in discriminating between traditional spambots, social spambots, and genuine accounts? Even if Twitter users were generally capable of distinguishing between traditional spambots and genuine accounts, they might still find it difficult to spot social spambots in the wild. If confirmed, this would provide additional evidence of the evolutionary step characterizing the new social spambots with respect to traditional ones. To answer these research questions, we asked a large set of real-world users to classify the accounts in our datasets. To obtain a large and diverse set of users, we recruited contributors from the CrowdFlower 4  crowdsourcing platform.
Thus, by asking contributors to (re-)classify our datasets, we are actually evaluating their ability to spot the different types of accounts. Enforcing results reliability. We only recruited contributors who were tech-savvy and Twitter users themselves, in order to be reasonably sure about their knowledge of Twitter and its dynamics. Furthermore, we required each account to be classified by at least 3 different contributors, with the final class decided by majority voting. We also fixed to 100 the upper threshold of the number of accounts that a single contributor could classify. In this way, we have obtained redundant results from a broad set of contributors. Then, in order to further guarantee the reliability of our crowdsourcing results, we designed a set of "test" (or "gold") questions aimed at evaluating the quality of contributors' answers.
A test question is one for which the correct answer is already known by the system. Within the crowdsourcing platform, such questions are indistinguishable from standard ones and are randomly mixed among all the questions, so that contributors cannot know whether they are answering to a test or to a standard question. Contributors' answers to test questions were checked against the known correct answers. Only the trusted contributors who answered correctly to more than the 70% of the test questions have been considered in our study. Our test questions consist of accounts whose nature is "easily" recognizable, and specifically: (i) a set of traditional spambots sampled from the dataset of Yang et al. [43] , (ii) a subset of genuine accounts, and (iii) a set of suspended, deleted, and protected accounts.
contributors the possibility to evaluate crowdsourcing campaigns for: (i) clarity of instructions, (ii) fairness of the test questions, (iii) ease of the task, and (iv) appropriateness of the payment. Out of the 247 participating contributors, 60 of them (∼ 24%) evaluated our campaign, leading to a convincing aggregated score of 3. 7/5, as shown in detail in Table  6. Our campaign costed us 410 US$ in total. The most interesting results of our crowdsourcing campaign are undoubtedly related to the detection performance of our human contributors. As reported in Table  4 , overall, the human annotators obtained an accuracy of less than 0. 24 on the social spambots, with more than 1,000 False Negatives (FN), meaning that contributors classified more than 1,000 accounts as genuine, when they actually belonged to the dataset of the last generation of spambots.
Annotation guidelines for spambots detection. Despite the recent advances in machine learning-based detection systems, manual verification of accounts to assess their degree of automation is still carried out by platform administrators  [15]. In  [41] , it is reported that human experts "consistently produced near-optimal results" on a dataset of traditional spambots. However, the results of our crowdsourcing experiment confirmed that the traditional "account-byaccount" annotation process used by human workers to evaluate social media accounts is no longer viable when applied to the detection of the novel wave of social spambots. Given the importance of manual annotation for the creation of ground-truth datasets and for double-checking suspicious accounts on social networking platforms, we call for the adoption of new annotation methodologies that take into account the similarities and synchronized behaviors of the accounts.
We have adopted a practical implementation of this methodology to annotate our datasets of social spambots. In particular, we have compared the timelines of large groups of accounts, in order to highlight tweeting similarities among them. By comparing the behaviors of different accounts, rather than by analyzing them one by one, we were able to spot the social spambots among all the collected accounts, as thoroughly described in Section 2. Therefore, we envisage the possibility to adopt this methodology, as well as similar ones, in order to safeguard the manual annotation process from elusive social spambots.
Specifically, it employs off-theshelf supervised learning algorithms trained with examples of both humans and bots behaviors, based on the Texas A&M dataset  [25]  with 15,000 examples of each class and millions of tweets. Similarly to most already established techniques, BotOrNot? performs its analyses on an accountby-account basis. Despite being specifically designed for the detection of social spambots, authors state that the detection performances of BotOrNot? against evolved spambots might be worse than those reported in  [14]. Here, we aim at evaluating this point by querying the BotOrNot? service with our sets of genuine and social spambot accounts. As shown in Table  7 , BotOrNot? achieves rather unsatisfactory results for the accounts of both test set #1 and test set #2 (such datasets are described in Table  1 ).
We have reproduced such a classifier by implementing and computing all the features proposed in  [43] , and by training the classifier with its original dataset. Results in Table  7  show that the system fails to correctly classify the novel social spambots. Similarly to the results of the BotOrNot? service, the worst results of this system in both test set #1 and test set #2 are related to the Recall metric. This means that also this classifier labeled social spambots as genuine accounts. Unsupervised spambot detection via Twitter stream clustering. Our initial claim, supported by preliminary work  [15, 47] , is that social spambots might be so sophisticatedly designed to make it very difficult to distinguish them from genuine accounts, if observed one by one. If demonstrated, this claim would imply that supervised classification approaches are intrinsically worse than unsupervised ones for the detection of social spambots.
g. , retweets of genuine tweets and famous quotes). For this reason, an approach almost solely based on tweet content will not be able to achieve satisfactory results. Unsupervised spambot detection via graph clustering. The approach in  [2]  exploits statistical features related to URLs, hashtags, mentions and retweets. Feature vectors generated in this way are then compared with one another via an Euclidean distance measure. Distances between accounts are organized in an adjacency matrix, which is later used to construct an undirected weighted graph of the accounts. Then, graph clustering and community detection algorithms are applied in order to identify groups of similar accounts. Graph clustering is done by employing the Markov cluster algorithm (MCL)  [38]. We fully implemented this solution and we experimented with our datasets.
The detection of a tampered computation is performed by computing the Kullback-Leibler distance between the statistical distribution of a given reputation score for the computation under investigation with that of a referenceuntampered -computation. If such a distance exceeds a given threshold, the computation under investigation is labeled as tampered. To test this technique against social spambots, we have computed the statistical distribution of the two reputation scores used in  [40]  (join date and number of followers) for the genuine and the social spambots accounts of our datasets. The results are in figures 4(a) and 4(b). Whereas the genuine accounts feature distributions that almost uniformly span across the possible range of values, social spambots have anomalous distributions. Thus, the technique proposed in  [40]  is capable of spotting the differences between groups of genuine accounts and the new wave of social spambots.
Accounts that share a suspiciously long DNA substring are then labeled as spambots. Notably, although working at group level,  [13]  is capable of spotting single spambot accounts. For this reason, we have been able to compare this technique with the ones previously benchmarked in Table  7. Applying the technique to our datasets, the similarity curve of genuine accounts is significantly different from that of social spambots, as shown in Figure  4 (c). More specifically, as measured by the LCS metric, social spambots #1 and #3 feature a level of similarity much higher than that of genuine accounts. Results reported in Table  7  demonstrate that the digital DNA-based technique  [13]  achieves excellent detection performances. The compelling features of the emerging techniques listed in this section represent a fertile ground for fighting the novel social spambots.
Elastic reactions are treated with the ECIS code that uses spherical optical models from the RIPL2 database. The DHORIN database is composed of several monoenergetic simulation files that contain millions of nuclear reactions details about the secondary ions (nature, energy and direction). These ionizing particles are transported within the device and the deposition of energy is calculated in the sensitive regions using the SRIM database made of the stopping power of various ions as a function of energy  [7]. Next, the effect of the energy deposition in the device is treated. To do so, the tool divides each ion track into small fragments that spherically diffuse their charge carriers and can be collected by the drain electrode that is itself divided into elemental sections. Then, integrating the charge diffusion along the whole track and over the whole drain surface gives the current pulse that is generated by the nuclear reaction at the electrode of each transistor.
Acknowledgements We thank the anonymous reviewers for their insightful comments. We thank  Laura Kallmeyer  and  Kilian Evang  for providing us with the script for the discontinuous PTB. First author is supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the  Investissements d'Avenir program  ( ANR-10-LABX-0083 ). Second author, supported by a public grant overseen by the  French ANR  ( ANR-16-CE33-0021 ), completed this work during a CNRS research leave at LIMSI,  CNRS / Université Paris Saclay.
Participants Data were collected from 40 sighted subjects (31 men and 9 women), aged from 18 to 40 (M age = 23. 7; SD age = 5. 2). Many participants were people with a computer science background. All participants filled out a background questionnaire, which was used to gather information on personal statistics such as age and education level. Our sample was composed by 34 right-handers and 6 left-handers. All participants were naive with respect to the experimental setup and purpose of the experiment.
To complete the formulation, periodic boundary conditions are applied to solid surfaces, which are in contact with the boundary of the unit-cell. In order to render the solution unique, we impose constraints on average values of displacement  [12]  using penalty terms in weak formulation  [46]. The unit-cell domain has been meshed 2  using GMSH software  [47]  and equations (3. 3 -3. 4) has been solved using FreeFEM++  [48]. The sphere radius is R = 0. 3 l and the cylinder radius is r = 0. 1 l, which results in porosity θ = 0. 85. Assuming the Poisson's ratio to be ν = 0. 3 (due to the linearity of the problem, the solution is valid for any Young's modulus value E), the effective elasticity tensor for the cubic-symmetric poroelastic medium in Voigt notation  [49, p. 136 ] is C =                   • 10 -2 E.
1) respectively. The governing equations are discretized using finite-element-method (FEM). The simulation domain along with equations is defined in the FreeFEM++  [48]  software using uniform mesh spacing 6 ∆s = 0. 20 l. We implement the weak formulation and solve the time dependent problem in a fully implicit manner, where boundary conditions at the interface are enforced using Lagrange multipliers 7. The effective coefficients, such as elasticity tensor and permeability matrix, are taken from section 3. To render the validation with the resolved model feasible, we select a moderate scale separation parameter l/H = 0. 1.
(b) Validation to fully microscale-resolved simulations In order to validate the effective model, a three-dimensional (3D) lid-driven cavity problem is considered (same depth of H + d and length H as before), where the flow is periodic in y direction over length l, which is set by the pore-sctructure. Therefore, it is sufficient to consider the domain shown in Fig. 6b  (spanning over one microstructure in y direction) for direct numerical simulations (DNS). The domain shown in Fig. 6b  is defined using GMSH software  [47]  and meshed using spacing ∆s 1 = 0. 04 l at the solid skeleton and ∆s 2 = 0. 16 l at the boundaries of the cavity  9. We import the generated mesh into FreeFEM++  [48] , in which we define and solve the governing equations (2. 1-2. 3) with boundary conditions (2. 4, 4. 1).
Notice that a proposal's acceptance can be significantly enhanced when the posterior (  6 ) is conditional on summary statistics of data η(Y), rather than Y itself, and in such case we would consider ρ(η(Y), η(Y * )). However, in practice for dynamical models it is difficult to identify "informative enough" summary statistics η(•), but see  Martin et al. [2016]  and  Picchini and Forman [2015]. Another important problem with the strategy outlined above is that "blind simulation" for the generation of the entire time series X * is often poor. In fact, even when the current value of θ is close to its true value, proposed trajectories rarely follow measurements when (a) the dataset is a long time series and/or (b) the model is highly erratic, for example when latent dynamics are expressed by a stochastic differential equation (section 4.
Simulation studies Simulations were coded in MATLAB (except for R examples using the pomp package) and executed on a Intel Core i7-2600 CPU 3. 40 GhZ. Software code can be found online either at https://github. com/umbertopicchini/SAEM-ABC or as supplementary material in the version of this paper published on Computational Statistics (doi:10. 1007/s00180-017-0770-y), see also the footnote on page 2. For all examples we consider a Gaussian kernel for J j,δ as in (8). As described at the end of section 2. 1, in SAEM we always set γ k = 1 for the first K 1 iterations and γ k = (k -K 1 ) -1 for k ≥ K 1 as in  Lavielle [2014]. All results involving ABC are produced using algorithm 3 i. e. using trajectories selected via ABC-SMC. We compare our results with standard algorithms for Bayesian and "classical" inference, namely Gibbs sampling and particle marginal methods (PMM)  [Andrieu and Roberts, 2009]  for Bayesian inference and the improved iterated filtering (denoted in literature as IF2) found in  Ionides et al.
30 [2. 27,2. 35], σy = 1. 90 [1. 86,1. 91], which are very close to the ones in Table  1. Finally, notice that results are not overly sensitive to the way δ is decreased: for example, if we let δ decrease uniformly with steps of size 1/3, that is δ ∈ {2, 1. 67, 1. 33, 1} with δ = 2 for the first 50 iterations, then let it decrease every 50 iterations until δ = 1, we obtain (when M = 1, 000 and M = 200) σx = 2. 30 [2. 27,2. 33], σy = 1. 91 [1. 87,1. 95], compare with Table  1. We then perform 30 simulations with SAEM-SMC using the same simulated data and parameters starting values as for SAEM-ABC. As from Table  1  simulations for σ y converge to completely wrong values. For this case we also experimented with M = 5, 000 using M = 2, 000 but this does not solve the problem with SAEM-SMC, even if we let the algorithm start at the true parameter values.
Comparison with iterative filtering and a pseudo-marginal Bayesian algorithm We compare the results above with the iterated filtering methodology for maximum likelihood estimation (IF2,  Ionides et al. , 2015) , using the R package pomp  [King et al. , 2015]. We provide pomp with the same data and starting parameter values as considered in SAEM-ABC and SAEM-SMC. We do not provide a detailed description of IF2 here: it suffices to say that in IF2 particles are generated for both parameters θ (e. g. via perturbations using random walks) and for the systems state (using the bootstrap filter). Moreover, same as with ABC methods, a "temperature" parameter (to use an analogy with the simulated annealing optimization method) is let decrease until the algorithm "freezes" around an approximated MLE.
Note that the SAEM-ABC and SAEM-SMC do not require a non-central parametrization. The maximization step of SAEM smooths out the correlation between the proposed parameter and the latent state, and the numerical convergence of the algorithms still occur (this has also been noticed in previous papers on SAEM-MCMC, see  Kuhn and Lavielle, 2005, Donnet and Samson, 2008). Once more, we attempt estimating data produced with (σ x , σ y ) = (2. 23, 2. 23) (as in section 4. 1. 1) this time using Metropolis-within-Gibbs. Trace plots in Figures 8 show satisfactory mixing for three chains starting at three random values around (σ x , σ y ) = (6, 6). However, while the chains initialized at values far from the truth rapidly approach the true values, the 95% posterior intervals fail to include them, see Figure  9.
05 on the time interval [t 0 , 100]. The Euler-Maruyama scheme is given by X t+h = X t + Dose • K a • K e Cl e -Kat -K e X t h + (σ h • X t )Z t+h where the {Z t } are i. i. d. N (0, 1) distributed. The grid of generated values X 0:N is then linearly interpolated at sampling times {t 1 ,. , t 100 } to give X 1:n. Finally residual error is added to X 1:n according to the model Y j = X j + ε j as explained above. Since the errors ε j are independent, data {Y j } are conditionally independent given the latent process {X t }. Sufficient statistics for SAEM The complete likelihood is given by p(Y, X 0:N ; θ) = p(Y|X 0:N ; θ)p(X 0:N ; θ) = n j=1 p(Y j |X j ; θ) N i=1 p(X i |X i-1 ; θ) where the unconditional density p(x 0 ) is disregarded in the last product since we assume X 0 deterministic. Hence the complete-data loglikelihood is L c (Y, X 0:N ; θ) = n j=1 log p(Y j |X j ; θ) + N i=1 log p(X i |X i-1 ; θ).
Results We consider an experiment where 50 datasets of length n = 100 each are independently simulated using parameter values (K e , K a , Cl, σ, σ ε ) = (0. 05, 1. 492, 0. 04, 0. 1, 0. 1). All results pertaining SAEM-ABC use the Gaussian kernel (8). For SAEM-ABC we use a number of "schedules" to decrease the threshold δ. One of our attempts decreases the threshold as δ ∈ {0. 5, 0. 2, 0. 1, 0. 03} (results for this schedule are reported as SAEM-ABC(1) in Table  3 ): same as in the previous application, all we need is to determine an appropriate order of magnitude for the largest δ. As an heuristic, we have that the empirical standard deviation of the differences |Y j -Y j-1 | is about 0. 3. The first value of δ is used for the first 80 iterations then it is progressively decreased every 50 iterations.
Acknowledgements We thank anonymous reviewers for helping improve considerably the quality of the work. Umberto Picchini  was partially funded by the  Swedish Research Council  (VR grant  2013-5167 ). Adeline Samson  was partially supported by the  LabEx PERSYVAL-Lab  ( ANR-11-LABX-0025-01 ) funded by the  French program Investissement d'avenir. This work is published on Computational Statistics, doi: 10. 1007/s00180-017-0770-y.
What difference does it make? First, software systems are usually considered as discrete event systems and various RE methods are based on this (such as the transition axiom method  [3] , or Event-B  [4] ). Yet the inputs and outputs of a motor or the ambient temperature for example, are better modeled as continuous variables. Another difference is that physical systems directly interact with the physical world, which adds less predictable and less formalizable parameters. The Stimulus tool  [5] , developed by Argosim, aims to improve real-time requirements for embedded software by allowing to simulate these requirements. Stimulus focuses on real-time requirements while our work aims to include different types of requirements, including real-time ones. That being said, our work and the work by Argosim have relatively similar principles, notably the link between models (architecture models and state machines) and requirements.
1-Introduction Petri nets are formal models designed to describe and analyze the behavior of concurrent systems. Among the many kinds of systems where Petri nets may be introduced to formally describe a concurrent dynamics, distributed databases  [9]  and telecommunication networks  [5]  are two typical examples. Both examples involve temporal evolution on the one hand, and the paradigm of resource sharing on the other hand, where resources are "spatially" distributed. Requests for resources are local, in such a way that any two actions requiring disjoint sets of resources may be considered as parallel. Since their initial introduction in the 1960's, several variants of Petri nets have been studied. In this paper, we shall limit ourselves to 1-safe Petri nets, which we briefly define now. An unmarked Petri net is a triple N = (P, T, F ), where P and T are two finite and disjoint non empty sets of places and of transitions respectively, and F ⊆ (P × T ) ∪ (T × P ) is called the flow relation.
In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance in applications like carbon capture or methane storage by orders of magnitude by only modifying the pore structure. For these applications it is therefore important to identify the optimal pore geometry and use this information to find similar materials. However, the mathematical language and tools to identify materials with similar pore structures, but different composition, has been lacking. We develop a pore recognition approach to quantify similarity of pore structures and classify them using topological data analysis. This allows us to identify materials with similar pore geometries, and to screen for materials that are similar to given top-performing structures.
U nderstanding Big Data is a challenge social and natural sciences share. The need to handle huge amounts of data, often generated by the steady increase of available computing power, has inspired rapid development in big-data science. In chemistry and material science, new research initiatives (for example, the materials genome initiative  1, 2  ) have led to the generation of large databases of materials for different applications. We focus on nanoporous materials, such as zeolites  3  , metal organic frameworks (MOFs)  4  , zeolitic imidizolate frameworks (ZIFs)  5  and porous polymer networks (PPNs)  6. These materials are of interest in applications ranging from gas separation and storage, to catalysis. In each case one would like to tailor-make a material that is optimal for that particular application.