dataesr/scientific-paragraphs-categorization huggingface dataset:
statistical analysis Comparisons between two groups were performed using a twotailed unpaired Student's t test. Multiple groups were compared using a one-way analysis of variance followed by a Newman-Keuls multiple comparison post hoc test. Values for all measurements are expressed as mean ± SEM. P < 0. 05 was considered statistically significant. Statistical analyses were performed using GraphPad Prism 5. 0 (GraphPad Software, USA). Data are representative of at least two independent experiments.
In another biologically relevant assay, we assessed whether PAF induces ROS release by chicken macrophages. Using a fluorescent probe, we showed that both PAF (10 µM) and E. coli LPS stimulation for 2 h promoted enhanced ROS release by chicken BMDM. However, PAFR antagonist PCA 4248 reverted ROS release only in PAF-treated BMDM, revealing that ROS release is induced by PAF in a PAFR-dependent manner (Figures  3D, E ). Altogether these data suggest that PAF promotes two acute macrophage-related inflammatory events in a PAFR-dependent fashion and independent of any cytotoxic effect in chicken macrophages.
acKnOWleDgMenTs We thank  Nathalie Lallier  for her technical assistance on the experimental infection model and the personnel from the experimental unit  PFIE (Plateforme d'Infectiologie Expérimentale) at the Centre INRA Val de Loire (Nouzilly, France). We also thank  Prof. Mauro M. Teixeira  ( UFMG, Brazil ) and  Dr. Cristiana C. Garcia  ( IOC/Fiocruz, Brazil ) for valuable discussions on the experimental design setup.
Concerns regarding the vulnerability of automatic speaker verification (ASV) technology against spoofing can undermine confidence in its reliability and form a barrier to exploitation. The absence of competitive evaluations and the lack of common datasets has hampered progress in developing effective spoofing countermeasures. This paper describes the ASV Spoofing and Countermeasures (ASVspoof) initiative, which aims to fill this void. Through the provision of a common dataset, protocols, and metrics, ASVspoof promotes a sound research methodology and fosters technological progress. This paper also describes the ASVspoof 2015 dataset, evaluation, and results with detailed analyses. A review of post-evaluation studies conducted using the same dataset illustrates the rapid progress stemming from ASVspoof and outlines the need for further investigation.
There are two general directions, i. e. that involving ever-more robust and advanced ASV techniques and that involving dedicated spoofing countermeasures. Advanced ASV techniques are expected to provide greater inherent resilience to spoofed speech, whereas dedicated countermeasures offer the potential for explicit spoofing detection. Perhaps for this reason, the latter has attracted the greatest interest. The literature shows growing momentum behind the development of spoofing countermeasures, a comprehensive survey of which is presented in  [5]. All early investigations on developing spoofing countermeasures were conducted with purpose-collected datasets typically generated using a limited number of specially crafted spoofingattack algorithms. Furthermore, the datasets and countermeasures were usually developed by the same researchers.
Such practice was necessary to support early research, as there were no common benchmark datasets. This methodology nonetheless raises three concerns. First, only the use of common datasets can support reproducible research and meaningful comparison of results generated by different research teams. Second, a priori knowledge of a spoofing attack does not reflect the practical scenario in which the nature of the spoofing attack can never be known beforehand. Third, the development of countermeasures using only a small number of spoofing attacks or spoofing algorithms may not offer the greatest potential for generalisation of different or unforeseen attacks that will almost certainly be encountered in the wild. Common datasets and competitive evaluations to support the development of spoofing countermeasures for other biometric modalities have existed for some time.
They include the series of LivDet evaluations for fingerprint recognition  [8]  and a similar initiative for face recognition  [9]. As of 2014, Fig. 1. The ongoing, community driven Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof) initiative aims to advance the state-of-the-art in spoofing countermeasures for voice biometrics. Kicked off in 2013 as a special session of Interspeech 2013 and followed up by the creation of the Spoofing and Anti-Spoofing (SAS) corpus of speech synthesis and voice-conversion spoofing data  [6] ,  [7] , the first ASVspoof challenge was co-organized in 2015. The initiative lead to the submission of 16 countermeasure systems, all benchmarked and ranked using a common database, protocol, and metric. Results were disseminated at a follow-up special session of Interspeech 2015 that attracted wide participation from ASV, speech-synthesis, and voice-conversion communities.
Plans for the second edition in 2017 are based on community feedback and lessons learnt from the first edition. none encouraged the development of spoofing countermeasures for ASV. The need for a common dataset, protocols, and metrics  [10]  was the principal finding of a special session on spoofing and countermeasures for ASV  [4]  held at the INTERSPEECH 2013 conference in Lyon, France. The ASV Spoofing and Countermeasures (ASVspoof) initiative was created shortly afterwards. Its goal was to foster ASV spoofing countermeasures by (i) gathering the necessary expertise to produce and to make publicly available a large dataset of genuine and spoofed speech and (ii) organising competitive evaluations to benchmark different countermeasure solutions. The key milestones of the ASVspoof initiative are illustrated in the timeline of Fig.
1. Stemming from the special session at INTERSPEECH, the creation of a large dataset of genuine and spoofed speech  [6] ,  [7]  started later in 2013. Following refinements and improvements to the dataset and protocols, the first ASVspoof challenge  [11]   1  was organised as a special session at IN-TERSPEECH 2015 held in Dresden, Germany. To lower the cost of entry, thus, maximising participation, ASVspoof 2015 involved a spoofing-detection challenge in isolation from ASV. The challenge was based upon a common database of both genuine speech and spoofed speech generated using ten mainstream speech-synthesis and voice-conversion spoofingattack algorithms. Participants were tasked with designing spoofing-detection algorithms to distinguish between genuine and spoofed speech in accordance with a common protocol and to submit their detection scores to the organisers for evaluation and ranking.
This first edition was successful in attracting wide participation, advancing the state-of-the-art in spoofing countermeasures, and identifying directions for future research. This paper presents the first ASVspoof evaluation with details of the dataset, protocols, and metrics. Also included are brief descriptions of participants' systems, their respective results, including those obtained from system fusion, with detailed analysis. Results reported in the literature postevaluation are also described and show the rapid progress in spoofing detection brought about by the ASVspoof initiative. Directions for future research are then discussed before current ideas for subsequent editions of ASVspoof, the next of which is planned for 2017.
A. Spoofing-attack algorithms Work prior to the release of the ASVspoof 2015 database generally produced countermeasures using spoofed speech data generated with a single or small number of methods implemented by the same researchers. The focus on specific spoofing-attack algorithms and on the use of a priori knowledge is clearly unrepresentative of the real use-case scenario in which it is impossible to know the nature of a spoofing attack. To reflect more closely a real scenario in which a wide variety of unknown spoofing attacks can be expected, the ASVspoof 2015 database was created using ten state-of-the-art spoofing-attack algorithms. They were contributed by members of the speech-synthesis and voice-conversion communities; therefore, introducing a level of independence from the effort within the ASV community to develop countermeasures.
B. Data visualization Figure  2  provides an intuitive visualisation of the acoustic similarities between each spoofing-attack algorithm and genuine speech. This visualisation is based upon i-vector data, the extraction process described in  [21]. Briefly, each utterance in the ASVspoof 2015 database was first converted into a 600-dimensional i-vector  [22]  then projected into a twodimensional space using an optimized variant  [23]  of the tdistributed stochastic neighbour embedding (t-SNE) method  [24]. The i-vector extractor was trained on Mel frequency cepstral coefficient (MFCC) features. Raw i-vectors were then processed using standard post-processing operations: raw ivectors are first whitened, projected onto a unit sphere  [25] , and finally treated with within-class covariance normalization (WCCN)  [26].
The WCCN matrix was trained by treating data corresponding to the 10 spoofing-attack algorithms and Fig. 2. Visualisation of differences between genuine and spoofed speech in the ASVspoof 2015 database. Two-dimensional data were produced using a t-distributed stochastic neighbour embedding (t-SNE) algorithm applied to high-dimensional i-vector representations of each utterance. genuine speech as 11 distinct classes. The entire ASVspoof database (a total of 263,151 i-vectors from 11 classes) was used to train the whitening and WCCN matrices. The t-SNE algorithm was applied with a perplexity parameter set to 40. The visualisation in Figure  2  shows mostly well separated spoofing-attack algorithms. Only S3 and S4 overlap, but this is to be expected; they correspond to the same algorithm trained with different quantities of data.
D. Protocols The ASVspoof 2015 database was provided with protocols for three distinct subsets, namely training, development, and evaluation. In addition to genuine speech data, the training and development sets are composed of spoofed data generated from a subset of five spoofing-attack algorithms (S1-S5) referred to as known attacks. The evaluation set contains data generated with all ten spoofing-attack algorithms, where the additional algorithms (S6-S10) are referred to as unknown attacks. This strategy, whereby there is no training or development data for unknown attacks, was chosen to encourage the development of generalised countermeasures  [33]. The number of speakers and trials in each subset is summarized in Table  II  and described below. Training set -The training set includes 3750 genuine and 12625 spoofed utterances collected from 25 speakers (10 male, 25 female).
A. Strategy and evaluation rules Whereas it is the impact of spoofing and countermeasures on ASV performance that is of the greatest interest, ASVspoof 2015 focused exclusively on spoofing detection, that is, detection in isolation from ASV. The approach is illustrated in Figure  4. The focus on simplicity decoupled the evaluation of standalone spoofing detection from the complexities of integrated ASV. This strategy helped maximise participation, which then required no prerequisite expertise in ASV. The ASVspoof 2015 participants were allowed to submit scores for up to six systems -three for a common training condition and three for a flexible training condition. The common training condition restricts participants to the use of only the ASVspoof 2015 training-set data for countermeasure learning and optimisation.
B. Submissions The ASVspoof database was requested by 28 teams from 16 countries, with 16 teams returning primary submissions by the deadline. While 27 additional submissions were also received, this paper focuses on results for primary submissions only. A summary of each of the 16 primary systems is provided in Table  III  and discussed below. [35]  and instantaneous frequency  [36]. A GMM classifier with 128 components was learned separately for each feature set. Finally, the two GMM classifier scores were fused.
For example, Systems D and I exhibited much lower EERs (0. 003 and 0. 005%, respectively) than System A (0. 408%) for known attacks. However, the same systems produced EERs more than 2 and 3 times that obtained with System A for unknown attacks. One reason unknown attacks produced higher EERs is that countermeasures were not reliable in detecting one of the unknown attacks (S10), the only waveform concatenation approach to speech synthesis used in the creation of the ASVspoof 2015 dataset. As can been seen from Table  IV , even the best EER for S10 was as high as 8. 49%. In general, the results confirm the importance of developing generalised countermeasures.
A. Post-evaluation progress Reviewed below are post-evaluation studies conducted with the ASVspoof 2015 database. They are categorised in terms of their focus on features and classifiers. The results are discussed afterwards. a) Features: An extensive evaluation of three different categories of features is reported in  [67]. They include shortterm power spectrum features, short-term phase features, and spectral features with long-term processing. Static and dynamic coefficients are also evaluated separately. Dynamic coefficients are found to be more useful than static coefficients. This is reasonable since voice-conversion and speech-synthesis techniques may not properly model the dynamic characteristics of speech. A comparison of six different feature parametrisations is reported in  [68].
The effect of mismatch between training and evaluation data is evaluated through a cross-database assessment in  [85]. The ASVspoof 2015 database and the similarly named AVspoof database  [86]  are used alternatively for training/testing. The results indicate that countermeasures that are effective on matched conditions are much less effective in the case of mismatched conditions, another example of poor generalization. Other studies also assess the impact of spoofing and more importantly, of countermeasures on ASV. To date, the only study that investigates their integration through experiments with the ASVspoof database is  [87]. This evaluation was carried out using a joint ASV+CM protocol. Five independent countermeasures were evaluated and fused in a standalone spoofing detection task.
This study compares the performance of 100 native English listeners to an automatic approach using a combination of MFCCand CNFP-based detectors. Both MFCCs and CNPFs include 18 dimensional static features, their deltas and delta-deltas. The results indicate that, on a subset of the ASVspoof 2015 database, automatic detectors outperform human listeners for all spoofing attacks, except S10. This finding would suggest that automatic spoofing-detection algorithms and human listeners use different cues to distinguish spoofed and genuine speech. This supports similar findings previous studies  [92] ,  [93]  found for voice imitations. This implies that there is a potential acoustic feature that can detect S10, and more recent studies, such as  [74] , have found features useful for detecting S10 spoofing attacks.
c) Impact on ASV: while the threat of each spoofing attack included in the ASVspoof 2015 database was validated using a state-of-the-art i-vector PLDA ASV system, the evaluation focused exclusively on spoofing detection independent of ASV. The evaluation of integrated spoofing countermeasures and ASV would require the joint optimisation of combined classifiers. Furthermore, different strategies can be adopted for system combination, e. g. via a simple series or parallel score fusion or through the joint modelling of speaker and spoofing characteristics  [98]. The evaluation of integrated spoofing countermeasures would thus hamper the meaningful comparison of different countermeasure technologies, where the latter would otherwise be the only difference between two experiments and results. Even so, the impact of spoofing countermeasures on ASV reliability is the primary interest.
The microphone can even be bypassed with the coupling of spoofing-attack data directly to the transmission line  [99]. Similar to the NIST speaker-recognition evaluations, future editions of ASVspoof may include multiple microphone and channel conditions, perhaps including different bandwidth conditions, to study the effects on countermeasure performance. The consideration of transmission channel (e. g. telephone, mobile, VoIP) and codec variability are important since codecs, such as CELP and GSM, are technically similar to speech-waveform-generation methods used for speech synthesis and voice conversion. Accordingly, the application of speech coding may cause current countermeasures to label genuine speech as spoofed speech. Physical access scenarios may also be addressed in the future through the re-recording of spoofing attacks with a fixed microphone.
e) Additive noise and reverberation: recent studies [100],  [82] ,  [83]  indicate that some countermeasures offer little resistance to additive noise, with spoofing-detection performance degrading much more rapidly as a function of falling signal-tonoise ratio than is typical for ASV performance. This suggests either an intrinsic difficulty in the problem or what is possibly the result of countermeasures over-trained to the ASVspoof database, which consists of technically high-quality speech. The impact of reverberation has also been investigated  [83]. With many logical and physical access scenarios offering the potential for adverse acoustic conditions, these effects warrant further consideration in the future. f) Passive countermeasures: voice conversion and speech synthesis are perhaps the two forms of spoofing attacks that fundamentally necessitate active countermeasures such as all those discussed in this article.
They are not the only line of defence, however. Passive countermeasures, such as prompted-text ASV or challenge-response mechanisms, can provide an alternative or added protection, especially from replay spoofing attacks. Currently lacking is an objective and scientific approach to validate their potential. While the latter is self-evident, the evaluation of passive countermeasures is likely to be much more complex than for active countermeasures. Nonetheless, they warrant greater attention in the future. g) Evaluation metrics: for simplicity, ASVspoof 2015 used the EER as the sole evaluation metric. The EER is a 'threshold-free' metric in the sense that the detection threshold need not be optimised as part of the evaluation. Instead, it is set with the use of ground-truth knowledge, namely the genuine or spoofed speech labels for the dataset under evaluation.
In the ASVspoof2015 database, the STRAIGHT vocoder was used for eight out of the ten spoofing-attack algorithms to generate high-quality speech waveforms. It is obviously important to include not only high-quality vocoders but also other types of vocoders and speech-waveform-generation methods in the future. A number of such topics will be considered for study in the second edition of ASVspoof being held in 2017. Appropriate standard anti-spoofing systems and/or features may be provided as publicly available baselines. Interested readers are invited to follow progress via the challenge website  14.
VI. CONCLUSIONS We described the ASVspoof initiative and the first common dataset, protocols, and metrics to foster progress in the field. A comprehensive overview of the database was provided and a new visualisation of differences between genuine and spoofed speech illustrated in a new light the challenge in developing effective spoofing countermeasures. While all ten spoofingattack algorithms used to generate the ASVspoof 2015 dataset were successful in circumventing a state-of-the-art ASV system, the results of 16 different systems submitted to the evaluation show that some are easily detected. Others prove more difficult to detect with evaluation results showing recurrent weaknesses to some attacks. While fusion experiments have the potential to improve performance, vulnerabilities remain.
SUMMARY OF SPOOFING-ATTACK ALGORITHMS IMPLEMENTED IN CHALLENGE DATABASE. S1 TO S5 ARE CONSIDERED KNOWN ATTACKS, EXAMPLES OF WHICH ARE AVAILABLE FOR SYSTEM DEVELOPMENT. S6 TO S10 ARE CONSIDERED UNKNOWN ATTACKS SEEN ONLY IN THE EVALUATION SET. HERE "TRAIN" MEANS THE TRAINING SET, "DEV" MEANS THE DEVELOPMENT SET, AND "EVA" MEANS THE EVALUATION SET.
ACKNOWLEDGEMENTS The authors would like to thank the following for their help in creating the ASVspoof 2015 database:  Dr. Daisuke Saito  ( University of Tokyo ),  Prof. Tomoki Toda  ( Nagoya University ),  Mr. Ali Khodabakhsh  ( Ozyegin University ),  Dr. Cenk Demiroglu  ( Ozyegin University ),  Dr. Linghui Chen  and  Prof. Zhen-Hua Ling  ( University of Science and Technology of China ). The authors would also like to thank the participants of ASVspoof 2015 and those who contributed to the associated special session held at Interspeech 2015.
The work presented in this paper was partially supported by  EPSRC  through  Programme Grants   EP/I031022/1  ( NST)  and  EP/J002526/1  (CAF), by the  Core Research for Evolutional Science and Technology (CREST)  from the  Japan Science and Technology Agency (JST)  (uDialogue project), by  MEXT KAKENHI  Grant Numbers ( 26280066 ,  26540092 ,  15H01686 ,  15K12071 ,  16H06302 ), by the  Academy of Finland  (project no. 253120  and  283256 ) and by the  Scientific and Technological Research Council of Turkey (TUBITAK) , project no. 115E916. The paper reflects some results from the  OCTAVE  Project (# 647850 ), funded by the  Research European Agency (REA)  of the  European Commission , in its framework programme  Horizon 2020. The views expressed in this paper are those of the authors and do not engage any official position of the  European Commission.
The ASVspoof database is freely available under a creative commons (CC-BY) license and can be downloaded from http://dx. doi. org/10. 7488/ds/298. The ASVspoof 2015 database is a subset of the Spoofing and Anti-Spoofing (SAS) corpus [6] , [7]  which is also freely available online at http://dx. doi. org/ 10. 7488/ds/252 and also under a creative commons (CC-BY) license.
2) TIME-VARYING DYNAMIC TOPOGRAPHY The satellite dataset used in this study is the Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) daily gridded sea level anomalies (SLA) from Ssalto/Duacs, downloaded from Copernicus Marine Services (Ssalto/Duacs 2014;  Pujol et al. 2016). We use delayed-mode dynamic topography for the period 1993-2015, with daily output, giving 8035 data records. These data are mapped to a 1 /48 Mercator grid using optimal interpolation of along-track data series based on the REF dataset, which uses two satellite missions [Ocean Topography Experiment (TOPEX)/Poseidon/European Remote Sensing Satellite (ERS) or Jason-1/Envisat or Jason-2/Envisat] with consistent sampling over the 21-yr period. Data are corrected for instrumental errors, atmospheric perturbations, orbit errors, tides, inverted barometer bias, and aliased fast barotropic signals (periods of less than 20 days).
Step 4: The inverse wavelet transform is used to reconstruct the SSH transect, and gradient thresholding is applied to determine the front locations. Following  Graham et al. (2012) , the gradient threshold is set to 0. 1 m per 100 km. Sensitivity tests to variations of this parameter have been performed and are included in appendix C. This algorithm has been implemented in the opensource Python language, and the code is freely available from the author's Github repository (see appendix A). The advantage of the WHOSE method is that by filtering the noise induced by eddies and other smallscale motions, front positions can be determined from instantaneous snapshots of ADT, even in regions of high eddy activity. In comparison, naive gradient thresholding (i. e. , without the filtering steps) is unable to reliably detect fronts in regions with intense eddy activity due to a large number of false positive errors  (Chapman 2014) , and methods such as the skewness/ kurtosis criteria of  Hughes et al.