dataesr/scientific-paragraphs-categorization huggingface dataset:
Reported Threats In this article, we refer to "threats" as self-evaluations of threat provided by a participant. Even though threat assessments may depend on some objective features of their environment, participants always construct a subjective representation of the facts when judging, deciding  (Kahneman and Tversky, 1979) , or perceiving risks  (Slovic et al. , 2005). Actual income reduction, or job loss, or other types of objective data were not measured here. They remain outside the scope of this paper, even though some of them may have been present in participants' minds when they answered. Threats were considered in terms of two domains: health and economic situation. The first domain, health, was addressed through three items. In the first item, participants answered to "Do you feel exposed to contamination from the virus?" using a five-point Likert scale (1 = "Absolutely not", 2 = "Low exposure", 3 = "Maybe or maybe not", 4 = "Yes, quite exposed" and 5 = "Yes, absolutely").
Data Analysis We used R (Version 4. 0. 2; R Core Team, 2020) for all our analyses. All data and analyses can be found in an open repository of the Open Science Framework website: https://osf. io/45aq3. In order to account for the longitudinal nature of the data, we tested our hypotheses using linear mixed models with the lmer function of the lme4 package  (Bates et al. , 2015). Sample sizes in each wave are presented in Table  1. The data from the 470 selected participants were used in all analyses. Intercepts were the only random parameters in all models (models that included "time" as a random parameter did not converge). Reported economic and health threat variables were time-dependent, that is, they were measured in each wave of the study. We computed the intra-class correlations for time-dependent variables (including the outcomes) using the ICCbare function of the ICC package  (Wolak et al.
Acknowledgments  K. J. Sanchez  and  G. C. Roberts  acknowledge the support of  NSF  Grant No. AGS-1660374. M. Diao  acknowledges the support of  NSF   AGS-1642291  and  NSF   OPP-1744965  grants and the  NCAR ASP Faculty Fellowship. We thank the  UCAR/ NCAR-Earth Observing Laboratory  and the flight crew for all the work done to obtain the measurements used in this manuscript.
Data Availability Statement SOCRATES data are available through the following EOL UCAR repository: https://data. eol. ucar. edu/mas-ter_lists/generated/socrates/ (Sanchez & Roberts, 2018 ; UCAR/NCAR-Earth Observing Laboratory, 2018; UCAR/NCAR-Earth Observing Laboratory, 2019a; UCAR/NCAR-Earth Observing Laboratory, 2019b). The VCSEL water vapor QC/QA data can be accessed online (at https://data. eol. ucar. edu/dataset/552. 052).
All models were fitted with the function MCMCglmm (R package MCMCglmm;  Hadfield, 2010). Learning score, considered as an ordered factor, and copying probability were fitted with "threshold" families. We used the default wide Normally distributed priors for fixed terms (µ = 0, V = 10 10 ), and expanded priors (V = 1, υ = 1, α. µ = 0, α. V = 1,000) for random terms. We fixed the residual variance to 1 in all models  (Hadfield, 2010). We parameterized all models to obtain an effective sample size >2,000 and autocorrelation levels <0. 1. We used 3 × 10 6 iterations, a burn-in of 3 × 10 5 and a thinning interval of 800 for models fitting learning score, and 10 5 iterations, a burn-in of 10 4 and a thinning-interval of 20 for models fitting copying probability. Convergence was assessed visually and by running Gelman and Rubin's convergence diagnosis tests on three MCMC chains with different starting points (gelman.
FUNDING This work was funded by the  ANR  ( Evol-Cog  project,  ANR-19-CE02-0007 ), the  Centre National pour la Recherche Scientifique (PICS)  and the  Region Rhône-Alpes  ( CIBLE programme ) to BD; the  NSERC (postdoctoral fellowship) , the  ABS  (student research award), the  BOU  and the  BES  (research grants), and the  SCO (Fred Cook award)  to LC; the  Ministère de l'Enseignement Supérieur et de la Recherche , the  Department of Ecology and Genetics  from  Uppsala University and Stiftelsen för Zoologisk Forskning , the  Région Auvergne Rhone-Alpes  (Explora'Doc mobility grants) and by the  University of Lyon  ( ATER fellowship  and IDEX mobility grant) to JM; the  Pearcy Sladen Memorial Trust and Carnegie Trust  (travel grants), and the  BOU  and  BES  (research grants) to PB.
Acknowledgements We would like to thank  Valeo  for funding this study and allowing us to use this compressor for further surge studies. We also thank the  BTS Arras and BTS La Joliverie  trainees as well as the engineering students of Cnam who made it possible to carry out these experiments successfully. The study was conducted within the  frame of project  " Amélioration de l'efficacité énergétique des véhicules routiers par optimisation du couplage moteur-turbocompresseur " funded by joint research program  AUF-FRS 2018  ( КП-06-Франкофония/3 ).
Treatment of PA14 With Different Lactonases Leads to Distinct Proteomic Profiles To further investigate the specific effects of signal disruption by the two lactonases and get a broader picture of the differences observed on phenotypes, we conducted proteomic analyses on P. aeruginosa PA14 cultures. A total of 515 (8. 7%) out of the 5,886 proteins of PA14 were detected and identified (Uniprot database). The relative abundances of 210 proteins were significantly changed (fold change ≥ 2 and p value < 0. 05) in at least one of the six possible comparisons (Supplementary Dataset S1). This number is in the same range as previous reports investigating QSI effects by proteomic and transcriptomic analyses  (Hentzer et al. , 2003; Nalca et al. , 2006; O'Loughlin et al. , 2013; Sethupathy et al. , 2016).
Ion components (retention time, ion mobility drift time and parents/fragments m/z values) were collected from raw data using the UNIFI software (version 1. 9. 3, Waters). Structures were targeted as follows: 0. 2 min retention time window, 2% CCS tolerance (experimental CCS values were 165/167 Å 2 for HHQ/PQS), 3 ppm m/z tolerance on parent [M + H] + adducts (m/z 244. 1696/260. 1645 for HHQ/PQS) and 10 mDa m/z tolerance on predicted fragments (including m/z 159. 0679/175. 06205 for HHQ/PQS). For calibration, HHQ/PQS stock solutions were prepared in methanol from pure standards (>96%; Sigma Aldrich). Culture medium was spiked with both compounds at concentrations ranging from 2 nM to 20 µM for HHQ and from 4 nM to 40 µM for PQS, every 10-fold. The molecules were extracted in the same conditions as previously described and the calibration curve was fitted point to point in order to estimate the concentrations of HHQ/PQS compounds (<15% deviation on controls).
RNA Extraction and QS Gene Expression Measurement From 200 µL of culture cells pellet, RNA was extracted and purified with PureLink TM RNA Mini Kit (Invitrogen) and residual DNA were digested with TURBO DNA-Free TM kit (Invitrogen). Then cDNA was synthesized using TaqMan TM Reverse Transcription Reagents (Applied Biosystems) and provided random hexamers. Eventually, qPCR was realized using LightCycler R 480 SYBR Green I Master (Roche), and a CFX96 Touch TM Real Time PCR Detection System (BioRad). Primers used are indicated in Supplementary Table  S1. The resulting data for each gene were normalized using housekeeping gene recA expression and analyzed using the 2 -Ct method  (Schmittgen and Livak, 2008). The results were plotted as relative expression level by dividing each 2 -Ct values by the mean of the control without added protein.
Proteomic Data Processing and Analysis The acquired files were imported into Progenesis QI software Version 2. 0 (Non-linear Dynamics, Newcastle, United Kingdom) for label-free quantification analysis. The data were aligned automatically and normalized. Processing parameters were 150 counts for the low energy threshold, 30 counts for the elevated energy threshold. The database used to identify peptides contains the protein sequences of Pseudomonas aeruginosa PA14 (TrembL, 25/04/2017, 5,886 sequences). Search tolerance parameters were: peptide and fragment tolerance, 15 ppm, FDR < 1%; Minimum Ion matching requirements were three fragments per peptide, seven fragments per protein and two peptides per protein. The enzyme specificity was trypsin allowing 1 missed cleavage, the accepted modifications were carbamidomethyl of cysteine (fixed), oxidation of methionine (variable), carbamyl of lysine and N-terminal (variable), deamidation (variable) of asparagine and glutamine.
The protein normalization was performed according to the relative quantitation using non-conflicting peptides. To determine the significance of changes between samples, a significant ANOVA (p value < 0. 05) and a fold change superior to 2 were used as the thresholds to define differently expressed protein. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Perez-Riverol et al. , 2019)  partner repository with the dataset identifier PXD017421. The principal component analysis (PCA) was performed on normalized data using SIMCA 14. The data were Pareto scaled, autofitted for principal components and the Hotelling's T 2 was used to assess the possible presence of outlier. For the heat map, the logarithm with base 10 (log 10 ) of the fold change was calculated.
ACKNOWLEDGMENTS The authors would like to thank  Prof. Bernard Lascola  and  Lina Barrassi  for their advice on the amoeba virulence assay,  Dr. Raphael Lami  for providing AHL reporter strains and  Prof. Sophie Bleves  for the E. coli competition assay. The authors also thank  Nathan Hoekstra  and  Sara Maitland  for corrections on the manuscript.
FUNDING This work was granted by a project  RAPID (LACTO-TEX)  from  Direction Générale de l'Armement (DGA, France). This work was also supported by " Investissements d'avenir" program  (Méditerranée Infection  10-IAHU-03 ) of the  French Agence Nationale de la Recherche (ANR). BR is a Ph. D. student granted by the " Emplois Jeunes Doctorants" program  of  Région Provence-Alpes-Côte d'Azur (PACA, France). ME was supported by the  MnDrive initiative.
Worse, such practice impedes the industry's growth and its agility to timely market sophisticated virtual and augmented environments with higher quality user experience. Challenges. Introducing automation to XR testing would greatly benefit the industry, but to get there, the following key challenges need to be addressed first: 1) Fine-grained interaction space. More traditional interactive applications such as a webshop or a spreadsheet give the user a strictly controlled and limited number of interaction choices. XR systems more accurately Funded by EU ICT-2018-3 H2020 Programme, grant nr. 856716 and by national funds through FCT, Fundacão para a Ciência e a Tecnologia, under project UIDB/50021/2020 Fig. 1. A game called Space Engineers as an example of an XR system, featuring advanced 3D worlds and an elaborate system to construct sophisticated custom objects, from simple solar panels to a complete space station.
Cognitive associations in Early Adolescence Data from 551 adolescents recruited by the IMAGEN consortium (https://imageneurope. com) were included. First, global measures of cortical surface area and scores from three WM tasks were entered into a bLCS model. A strict measurement invariant, bivariate-LCS model (Fig. 1 ), with SES and EduYears-PGS as covariates of interest, fit the data well, RMSEA =. 031, CFI =. 997. We found that SES and EduYears-PGS were significantly and positively correlated (r =. 27, p <. 001). SES had a positive and significant association to WM, even when correcting for EduYears-PGS (β =. 23, p <. 001). Splitting SES into subcomponents revealed that only parental education was significantly associated with WM. EduYears-PGS also had a positive, independent, although weaker relationship, with WM at 14 (β =.
Study description IMAGEN is a European multi-site longitudinal genetic and neuroimaging study. All procedures were approved by each of the sites' (Berlin, Dresden, Dublin, Hamburg, London, Mannheim, Nottingham, Paris) ethics committees. Written informed consent was obtained from the adolescents and parents involved in the study. Our study uses data from the first two neuroimaging waves, at ages fourteen (14. 44, SD = 0. 38) and nineteen (19. 01, SD = 0. 72). See Schumann and colleagues  14  for more information on IMAGEN protocols and inclusion/exclusion criteria. For subjects to be included in our study, they had to be of European ancestry (due to limitations of the imputations and possible inferences for creating the EduYears-PGS) and have no siblings included in the study (the few sibling pairs were only present due to mistakes in data collection).
edu/wpcontent/uploads/2012/07/ENIGMA2_1KGP_cookbook_v3. pdf). We then used this genotype data to estimate EduYears-PGS in each participant based on the effect sizes of thousands of SNPs discovered by the most recent GWAS on educational attainment  13. We first obtained the summary statistics of the GWAS from the Social Science Genetic Association Consortium (https://www. thessgac. org/data). We decided which significance threshold to use in our sample by performing high resolution scoring in PRSice-2  48  based on the phenotype of WM (from the latent factor of our three cognitive tasks). The threshold that optimally explains the variance in WM resulted in an EduYears PGS containing 5709 SNPs. To guard against overfitting, we performed 1 million permutations and obtained a significant empirical p-value of our estimate (empirical p = 0.
3. 6. 0) package Lavaan (v. 0. 6-3)  60, 61. Missing follow-up behavioral data was imputed under the assumption of missing at random (see SI Appendix, Fig. S3 )  62. We assessed model fit using the comparative fit index (CFI; fit > 0. 95) and the root mean square error of approximation (RMSEA; fit <. 08)  63. The subjects' age and gender were regressed from all observed behavioral and neural measures in both timepoints before model fitting. For the hippocampus and amygdala analyses total intercranial volume was also corrected for. All models have strict measurement invariance; intercepts, loadings and error variance were constrained to be equal across time. Prior to fitting a bLCS model we assessed fit on respective measurement models. If estimates present Heywood cases (negative error variances) we left them unconstrained as long as the null hypothesis could not be rejected with the use of confidence intervals  64.
Acknowledgments This work received support from the following sources: Vetenskapsradet (Swedish research council;  2015-02850 ), Wenner-gren foundation ( UPD2018-0295 ),  National Institute of Health : ( Meaningful Data Compression and Reduction of High-Throughput Sequencing Data ") (1  U01 CA198952-01 ), The  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  Horizon 2020  funded  ERC Advanced Grant 'STRATIFY' (Brain network  based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the  Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways)  ( PR-ST-0416-10004 ),  BRIDGET  ( JPND: BRain Imaging, cognition Dementia and next generation GEnomics ) ( MR/N027558/1 ),  Human Brain Project  ( HBP SGA 2, 785907 ), the  FP7  project  MATRICS  ( 603016 ), the  Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions)  ( MR/N000390/1 ), the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London , the  Bundesministeriumfür Bildung und Forschung (BMBF  grants  01GS08152 ;  01EV0711 ;  Forschungsnetz AERIAL 01EE1406A ,  01EE1406B ), the  Deutsche Forschungsgemeinschaft (DFG  grants  SM 80/7-2 ,  SFB 940 ,  TRR 265 ,  NE 1383/14-1 ), the  Medical Research Foundation and Medical Research Council  (grants  MR/R00465X/1  and  MR/S020306/1 ), the  National Institutes of Health (NIH)  funded ENIGMA (grants  5U54EB020403-05  and  1R56AG058854-01 ).
Further support was provided by grants from: - ANR  (project  AF12-NEUR0008-01 -WM2NA ,  ANR-12-SAMA-0004 ), the  Eranet Neuron  ( ANR-18-NEUR00002-01 ), the  Fondation de France  ( 00081242 ), the  Fondation pour la Recherche Médicale  ( DPA20140629802 ), the  Mission Interministérielle de Lutte-contre-les-Drogues-etles-Conduites-Addictives (MILDECA) , the  Assistance-Publique-Hôpitaux-de-Paris  and  INSERM  (interface grant),  Paris Sud University IDEX 2012 , the  fondation de l'Avenir  (grant  AP-RM-17-013  ), the  Fédération pour la Recherche sur le Cerveau ; the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ),  U. S. A. (Axon, Testosterone and Mental Health during Adolescence ;  RO1 MH085772-01A1 ), and by  NIH Consortium  grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to Knowledge Centres of Excellence.
As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks.
The manuscript highlights that current IDS research covers only 33. 3% of our threat taxonomy. Current datasets demonstrate a clear lack of realnetwork threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets.
I. INTRODUCTION T HE world is becoming more dependent on connected devices, actuators and sensors, regulating the lives of millions of people. Furthermore, sensor data is expected to increase by around 13%, reaching 35% of overall data communication in 2020, reaching a peak of 50 billion connected devices and an increased monthly Internet traffic volume reaching 30 GB on average per capita compared to around 10 GB in 2016  [1]. While each device in an Internet of Things (IoT) system exchanges data, associated services often provide interfaces to interact with the collected data, often increasing the attack surface. Therefore, it is crucial to build robust tools to defend networks against security threats in modern IoT networks. Current detection tools are often based on outdated datasets that do not reflect the reality of recent/modern network attacks, rendering Intrusion Detection Systems (IDS) ineffective against new threats and zero-days.
To the best knowledge of the authors, there are currently no manuscripts that analyze the shortcomings of available networking datasets, nor provide a taxonomy of the current network threats and the associated tools used to carry out these attacks. The contributions of this research are threefold: • An evaluation of the limitations of the available network-based datasets and their impact on the development of IDSs • A review of the last decade's research on NIDS • A Threat taxonomy is presented, and categorized by: -The Threat Sources -The Open Systems Interconnection (OSI) Layer -Active or Passive modes The evaluation of current network-based datasets provides researchers with an insight of the shortcomings of the datasets presented when used for training against realworld threats. A threat taxonomy is derived from the datasets and current real-world networking threats.
This taxonomy serves two purposes-firstly, it strengthens our argument on the shortcomings of currently available datasets, but most importantly, it provides researchers with the ability to identify threats and tools underrepresented in currently available datasets. To facilitate this endeavor, we further map the current threats with their associated tools, which in turn can be used by research to create new datasets. The rest of the paper is organized as follows; Section II depicts the main differences between IDSs, the metrics to consider for their evaluation and the role of feature selection in building IDSs. Section III reviews IDSs of the past decade and their individual contributions are assessed. This section also evaluates the drawbacks and limitations of the available datasets. Section IV provides the threat taxonomy.
P recision = T P T P + F P To visualize the performance of an IDS, i. e. the trade-off between sensitivity (true positive rate) and fallout (true negative rate), AUC (Area Under The Curve) ROC (Receiver Operating Characteristics) also known as Area Under the Receiver Operating Characteristics (AUROC) curve is used  [11] -  [13]  Equation 4 represents the harmonic mean of precision and recall. F1 is better suited to represent the performance of an IDS, specially when dealing with imbalanced classes. F 1 = 2T P 2T P + F P + F N (4) Equation 5 provides Matthews correlation coefficient. It can only be used in binary IDS in which incidents are classified as either attack or normal. M cc = (T P * T N ) -(F P * F N ) (T P + F P )(T P + F N )(T N + F P )(T N + F N ) (5) Equation 6 addresses the problem of calculating accuracy in imbalanced datasets.
Numerous datasets have a limited number of attacks' data compared to benign traffic, hence, the geometric mean of accuracy provides a more precise metric than overall accuracy measure  [14]. Space-Time aware evaluation is introduced by Feargus Pendlebury et al. [15]  to overcome both spatial and temporal biases. The authors introduced three constraints to be considered when splitting datasets. gAcc = √ a +ve. a -ve = Sensitivity (T P R). Specif icity (T N R) (6) Additionally, CPU consumption, throughput and power consumption are important metrics for evaluating IDSs. Specifically, these metrics are important for IDSs running on different hardware or with specific settings such as high-speed networks, or on hardware with limited resources. 2) Feature Selection and IDS "Feature Learning"  [10]  or "Feature Engineering"  [16]  plays an essential role in building any IDS in a way that chosen features highly affect the IDS performance.
Features are obtained using one of three processes; construction, extraction and selection. Selection involves filter, wrapper and embedded techniques  [17]. A classification of the features used in recent datasets is provided in  [4]. Different features representations (i. e. abstractions) are used to address different areas of threat detection. Some of them could be considered naïve when they contain basic network information. Others are considered rich when they represent deeper details  [16]. As highlighted by Rezaei and Liu  [18] , there are four main categories of networking features; time series, header, payload and statistical. Unlike header and payload features, time series and statistical ones are available for both encrypted and unencrypted traffic. The authors further discuss the shortcomings of current encrypted traffic classification research.